Pipeline run
8a2ffc73-8fe4-45b4-880f-080010e9e83d
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
Captured for admin review
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Data Engineer
CASE Aslug: data-engineer · id: 2 · source: db
Exact alias hit on data-engineer (1.0) — no other alias at this confidence; skill_top data-engineer 0.40 does not contradict
Job description
Experience: 5.00 + years Salary: Confidential (based on experience) Shift: (GMT+05:30) Asia/Kolkata (IST) Opportunity Type: Remote Placement Type: Full time Permanent Position (*Note: This is a requirement for one of Uplers' client - 1digitalstack.ai) What do you need for this opportunity? Must have skills required: Python, Java, Iceberg, Kafka, Apache Beam, Apache Flink, Apache pulsar, Spark, Trino, OLAP, ClickHouse, starrocks 1digitalstack.ai is Looking for: Role - Senior Data Engineer Experience - 5-7 Years Location - Remote (India) About 1DigitalStack.ai 1DigitalStack.ai combines AI and deep eCommerce data to help global brands grow faster on online marketplaces. Our platforms deliver advanced analytics, actionable intelligence, and media automation — enabling brands to optimize visibility, efficiency, and sales performance at scale. We partner with India’s top consumer companies — Unilever, Marico, Coca-Cola, Tata Consumer, Dabur, and Unicharm — across 125+ marketplaces globally. Backed by leading venture investors and powered by a 220+ member team, we’re in our $5–10M growth journey, scaling rapidly across categories and geographies to redefine how brands win on digital shelves. 🔗 Check out more at www.1digitalstack.ai About Role This is a high-impact, hands-on engineering role owning the core data systems that power our analytics, AI, and automation stack. You’ll work closely with the CTO and Engineering Leads and independently manage large, high-throughput data pipelines that process millions of events. Responsibilities : • Build and maintain high-throughput, real-time data pipelines using Kafka/Pulsar with Spark, Flink, and distributed compute engines. • Design fault-tolerant systems with zero-data-loss principles — checkpointing, replay logic, DLQs, deduplication, and back-pressure handling. • Implement data observability — quality checks, SLA alerts, anomaly detection, lineage, and metadata insights. • Design and manage Iceberg-based lakehouse tables (Polaris/Gravitino catalogs, schema evolution, compaction). • Build fast OLAP layers using ClickHouse / StarRocks. • Model data across bronze → silver → gold layers for downstream teams. • Migrate and modernize legacy pipelines into scalable, distributed workflows. • Orchestrate ETL workloads using Airflow, DBT, Dagster, SQLMesh. • Optimize SQL transformations and distributed execution across Trino/Spark. • Ensure strict security and governance across all data layers — access control, encryption, auditability. • Collaborate with backend, analytics, and platform teams for seamless data delivery. Requirements Core Technical Skills • Extremely strong SQL — window functions, query planning, optimization. • High comfort working with distributed & parallel workloads. • Hands-on experience with some-many of these technologies : Apache Spark, Apache Flink, Trino, Apache Kafka, Apache Pulsar, Apache Beam • Advanced experience in Python (preferred) or Java (strong fundamentals). • Strong understanding of Parquet, Apache Iceberg, and Iceberg REST catalogs (Polaris / Gravitino). • Experience with OLAP databases — ClickHouse / StarRocks. • Experience with semantic layers — Cube.js or similar. • Strong experience building pipelines with Airflow, DBT, Dagster, SQLMesh. Foundational Strengths • Solid understanding of data structures & algorithms — sorting, searching, memory models. • Strong grasp of OLTP vs OLAP, indexing, query execution, and storage formats. • Ability to debug distributed systems end-to-end (compute, storage, network, orchestration). • Familiarity with cloud environments, containerization (Docker), and monitoring. • Experience with large-scale data — high throughput, billions of rows, large parallel workloads. • Awareness of cost optimization in compute & storage. Good to Have • Experience with emerging stream processors — Dagster, RisingWave, Arroyo. • Kubernetes, Terraform, or cloud-native big-data stacks. Mindset • Strong ownership — takes systems from design → build → monitor. • Self-driven, independent, and comfortable making technical decisions. • High attention to reliability, data accuracy, and operational excellence. • Naturally grows into broader technical responsibility as the platform scales. Why 1DS is a great choice • High-trust, no-politics culture — we value communication, ownership, and accountability • Collaborative, ego-free team — building together is in our DNA • Learning-first environment — mentorship, peer reviews, and exposure to real business impact • Modern stack + autonomy — your voice shapes how we build • VC-funded & scaling fast — 250+ strong, building from India for the world How to apply for this opportunity? • Step 1: Click On Apply! And Register or Login on our portal. • Step 2: Complete the Screening Form & Upload updated Resume • Step 3: Increase your chances to get shortlisted & meet the client for the Interview! About Uplers: Our goal is to make hiring reliable, simple, and fast. Our role will be to help all our talents find and apply for relevant contractual onsite opportunities and progress in their career. We will support any grievances or challenges you may face during the engagement. (Note: There are many more opportunities apart from this on the portal. Depending on the assessments you clear, you can apply for them as well). So, if you are ready for a new challenge, a great work environment, and an opportunity to take your career to the next level, don't hesitate to apply today. We are waiting for you!
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
Aliases — catalog
- Kafka (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Event Stream Store
- Vendor
- Confluent
- License
- apache_2
- Year introduced
- 2011
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Kafka appears in many production JDs for event streaming and data pipelines, and remains a standard platform in cloud/vendor offerings (e.g., Confluent, AWS MSK), indicating broad hiring demand.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 3533
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Asynchronous Messaging and Event Streaming Catalog dimension db id 297
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Go Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Scala Backend Developer
-
Messaging and Background Jobs Catalog dimension db id 291
Library dimension (catalog)
Roles linked in library: PHP Backend Developer, Python Backend Developer, Ruby Backend Developer
-
Messaging and Event Streaming Catalog dimension db id 8
Library dimension (catalog)
Roles linked in library: Backend Developer, Data Engineer
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Apache Spark (CANONICAL)
- apache spark 3 (VERSION)
- spark (VERSION)
- spark 3 (VERSION)
- spark 3.x (VERSION)
- spark3 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Distributed Data Processing Framework
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2010
- Confidence
- 0.94
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3.x
Maturity reasoning: Apache Spark appears in many data engineering JDs and remains a standard for distributed ETL/ELT; its GitHub and vendor ecosystem activity stay strong, with Databricks and cloud platforms still promoting it.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 1021
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ETL and ELT Tooling Catalog dimension db id 24
Library dimension (catalog)
Roles linked in library: Data Engineer
Aliases — catalog
- Apache Spark (CANONICAL)
- apache spark 3 (VERSION)
- spark (VERSION)
- spark 3 (VERSION)
- spark 3.x (VERSION)
- spark3 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Distributed Data Processing Framework
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2010
- Confidence
- 0.94
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3.x
Maturity reasoning: Apache Spark appears in many data engineering JDs and remains a standard for distributed ETL/ELT; its GitHub and vendor ecosystem activity stay strong, with Databricks and cloud platforms still promoting it.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 1021
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ETL and ELT Tooling Catalog dimension db id 24
Library dimension (catalog)
Roles linked in library: Data Engineer
Aliases — catalog
- Apache Flink (CANONICAL) primary
- Apache Flink 1.20 (VERSION)
- Apache Flink 1.x (VERSION)
- Flink 1.20 (VERSION)
- Flink 1.x (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Stream Processing Framework
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2014
- Confidence
- 0.95
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 1.20
Maturity reasoning: Apache Flink appears in streaming/data-platform JDs, but far less often than Spark/Kafka; GitHub and job-market signals show a specialized real-time processing niche rather than broad hiring staple.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 94
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Stream Processing Systems Catalog dimension db id 25
Library dimension (catalog)
Roles linked in library: Data Engineer
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Airflow (CANONICAL) primary
- airflow 2 (VERSION)
- airflow-2 (VERSION)
- airflow2 (VERSION)
- airflow2.x (VERSION)
- apache airflow 2 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Workflow Orchestration Tool
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2014
- Confidence
- 0.95
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 2.x
Maturity reasoning: Apache Airflow appears in many data engineering job postings and is a common orchestration choice in production stacks; its GitHub activity and ecosystem remain strong, with no vendor sunset or clear replacement dominating JDs.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 130
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Workflow Orchestration for ML Pipelines Catalog dimension db id 54
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
Aliases — catalog
- dbt (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Analytics Engineering Framework
- Vendor
- dbt Labs
- License
- apache_2
- Year introduced
- 2016
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: dbt appears in many analytics engineer and data platform job descriptions, and its GitHub repo has strong adoption signals with widespread ecosystem support from major cloud/data vendors.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 89
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ETL and ELT Tooling Catalog dimension db id 24
Library dimension (catalog)
Roles linked in library: Data Engineer
Aliases — catalog
- Dagster (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Data Orchestration Tool
- Vendor
- Elementl
- License
- apache_2
- Year introduced
- 2019
- Confidence
- 0.95
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Dagster appears in a growing number of data engineering JDs and cloud vendor docs, but it is still far less common than Airflow/Prefect, indicating rising adoption rather than ubiquity.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 1161
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Data Pipeline Orchestration Catalog dimension db id 23
Library dimension (catalog)
Roles linked in library: Data Engineer
-
Workflow Orchestration for ML Pipelines Catalog dimension db id 54
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- SQL (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Query Language
- Vendor
- ANSI
- License
- unknown
- Year introduced
- 1974
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: SQL appears in a large share of data, backend, and analytics job descriptions and remains the default query language for PostgreSQL, MySQL, and cloud warehouses like Snowflake/BigQuery.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 97
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Pega Programming Languages & DSLs Catalog dimension db id 267
Library dimension (catalog)
Roles linked in library: Pega Developer
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
Aliases — catalog
- Python (CANONICAL) primary
- Python 2 (VERSION)
- Python 2.x (VERSION)
- Python 3 (VERSION)
- Python 3.10 (VERSION)
- Python 3.11 (VERSION)
- Python 3.12 (VERSION)
- Python 3.x (VERSION)
- py (VERSION)
- py2 (VERSION)
- py3 (VERSION)
- python 3 (VERSION)
- python 3.x (VERSION)
- python2 (VERSION)
- python3 (VERSION)
- python3.x (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- PSF
- License
- mit
- Year introduced
- 1991
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3
Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 96
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Security Scripting & DSL Languages Catalog dimension db id 248
Library dimension (catalog)
Roles linked in library: Cloud Security Engineer
-
Programming Languages Catalog dimension db id 1
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer
-
Programming Languages and Scripting Catalog dimension db id 59
Library dimension (catalog)
Roles linked in library: Cyber Security Engineer
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
-
Programming Languages for XR Catalog dimension db id 97
Library dimension (catalog)
Roles linked in library: AR/VR Engineer
-
Python Programming Catalog dimension db id 290
Library dimension (catalog)
Roles linked in library: Python Backend Developer
Aliases — catalog
- Java (CANONICAL) primary
- JDK (VERSION)
- JDK 10 (VERSION)
- JDK 11 (VERSION)
- JDK 12 (VERSION)
- JDK 13 (VERSION)
- JDK 14 (VERSION)
- JDK 15 (VERSION)
- JDK 16 (VERSION)
- JDK 17 (VERSION)
- JDK 18 (VERSION)
- JDK 19 (VERSION)
- JDK 20 (VERSION)
- JDK 21 (VERSION)
- JDK 5 (VERSION)
- JDK 6 (VERSION)
- JDK 7 (VERSION)
- JDK 8 (VERSION)
- JDK 9 (VERSION)
- Java 1.0 (VERSION)
- Java 1.1 (VERSION)
- Java 1.2 (VERSION)
- Java 1.3 (VERSION)
- Java 1.4 (VERSION)
- Java 1.5 (VERSION)
- Java 1.6 (VERSION)
- Java 1.7 (VERSION)
- Java 1.8 (VERSION)
- Java 10 (VERSION)
- Java 11 (VERSION)
- Java 12 (VERSION)
- Java 13 (VERSION)
- Java 14 (VERSION)
- Java 15 (VERSION)
- Java 16 (VERSION)
- Java 17 (VERSION)
- Java 18 (VERSION)
- Java 19 (VERSION)
- Java 20 (VERSION)
- Java 21 (VERSION)
- Java 5 (VERSION)
- Java 6 (VERSION)
- Java 7 (VERSION)
- Java 8 (VERSION)
- Java 9 (VERSION)
- Java11 (VERSION)
- Java17 (VERSION)
- Java21 (VERSION)
- Java8 (VERSION)
- OpenJDK 11 (VERSION)
- OpenJDK 17 (VERSION)
- OpenJDK 21 (VERSION)
- OpenJDK 8 (VERSION)
- java 11 (VERSION)
- java 17 (VERSION)
- java 21 (VERSION)
- java 4 (VERSION)
- java 5 (VERSION)
- java 6 (VERSION)
- java 7 (VERSION)
- java 8 (VERSION)
- java lts (VERSION)
- java-11 (VERSION)
- java-17 (VERSION)
- java-21 (VERSION)
- java-4 (VERSION)
- java-5 (VERSION)
- java-6 (VERSION)
- java-7 (VERSION)
- java-8 (VERSION)
- java11 (VERSION)
- java17 (VERSION)
- java21 (VERSION)
- java4 (VERSION)
- java5 (VERSION)
- java6 (VERSION)
- java7 (VERSION)
- java8 (VERSION)
- jdk 11 (VERSION)
- jdk 17 (VERSION)
- jdk 21 (VERSION)
- jdk 4 (VERSION)
- jdk 5 (VERSION)
- jdk 6 (VERSION)
- jdk 7 (VERSION)
- jdk 8 (VERSION)
- jdk11 (VERSION)
- jdk17 (VERSION)
- jdk21 (VERSION)
- jdk4 (VERSION)
- jdk5 (VERSION)
- jdk6 (VERSION)
- jdk7 (VERSION)
- jdk8 (VERSION)
- jvm21 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- Oracle
- License
- other_open
- Year introduced
- 1995
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 21
Maturity reasoning: Java is a hiring-pipeline staple with very high JD volume across enterprise backend, Android, and cloud roles; it remains widely supported by major vendors and frameworks like Spring.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 96
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Java Language and JVM Catalog dimension db id 279
Library dimension (catalog)
Roles linked in library: Java Backend Developer, Kotlin Backend Developer, Scala Backend Developer
-
Kotlin and Java Catalog dimension db id 161
Library dimension (catalog)
Roles linked in library: Android Developer
-
Native Mobile Languages Catalog dimension db id 274
Library dimension (catalog)
Roles linked in library: Native Mobile Developer
-
Pega Programming Languages & DSLs Catalog dimension db id 267
Library dimension (catalog)
Roles linked in library: Pega Developer
-
Programming Languages Catalog dimension db id 1
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
Aliases — catalog
- Parquet (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Format
- Sub-category
- Columnar File Format
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2013
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Widely used in data engineering and analytics; frequently appears in JDs for Spark/Databricks/Big Data roles and is a standard storage format in cloud data lakes.
Skill profile (library / DB)
- Skill nature
- STANDARD
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 4
- Sub-category id
- 87
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Data Serialization Standards & Protocols Catalog dimension db id 37
Library dimension (catalog)
Roles linked in library: Data Engineer
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Docker (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Containerization Tool
- Vendor
- Docker, Inc.
- License
- apache_2
- Year introduced
- 2013
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Docker is a hiring-pipeline staple: it appears in many DevOps, backend, and platform JDs, and remains a standard containerization tool alongside Kubernetes in production stacks.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 63
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Containerization and Image Builds Catalog dimension db id 152
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
Deployment and Cloud Platforms Catalog dimension db id 418
Library dimension (catalog)
Roles linked in library: Ruby Backend Developer
-
Deployment and Runtime Configuration Catalog dimension db id 13
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Go Backend Developer, PHP Backend Developer
Aliases — catalog
- Kubernetes (CANONICAL) primary
- Kubernetes 1.0+ (VERSION)
- Kubernetes 1.x (VERSION)
- Kubernetes v1 (VERSION)
- k8s (VERSION)
- kubernetes 1.x (VERSION)
- kubernetes latest (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Container Orchestration Platform
- Vendor
- Cloud Native Computing Foundation
- License
- apache_2
- Year introduced
- 2014
- Confidence
- 0.90
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 1.30
Maturity reasoning: Broadly adopted in cloud-native stacks; Kubernetes appears in a large share of DevOps/SRE job descriptions and is the default orchestration platform across major cloud vendors.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 557
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Container Orchestration Platforms Catalog dimension db id 134
Library dimension (catalog)
Roles linked in library: Cloud Architect, DevOps Engineer
-
Kubernetes for ML Workloads Catalog dimension db id 47
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
Aliases — catalog
- Terraform (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Infrastructure As Code Tool
- Vendor
- HashiCorp
- License
- mpl
- Year introduced
- 2014
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Terraform is broadly listed in DevOps/SRE/cloud JDs and remains a standard IaC tool across AWS/Azure/GCP; HashiCorp’s ecosystem and widespread GitHub usage signal strong market adoption.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 191
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Infrastructure & Security Automation Frameworks Catalog dimension db id 249
Library dimension (catalog)
Roles linked in library: Cloud Security Engineer
-
Infrastructure as Code Catalog dimension db id 132
Library dimension (catalog)
Roles linked in library: Cloud Architect, DevOps Engineer
-
Infrastructure as Code for ML Catalog dimension db id 57
Library dimension (catalog)
Roles linked in library: ML Engineer
Aliases — catalog
- Distributed Systems (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Distributed Systems
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common hiring requirement in backend/platform JDs at large tech firms; appears across AWS, Kafka, microservices, and systems roles, with strong GitHub/Stack Overflow activity and no sunset signal.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1035
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Platforms Catalog dimension db id 20
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer
-
Performance and Scalability Tuning Catalog dimension db id 11
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Node.js Backend Developer, PHP Backend Developer, Python Backend Developer
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- indexing (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Database Indexing
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Database indexing is a standard requirement in SQL/NoSQL job descriptions and core DB docs; it’s broadly expected for performance tuning rather than a niche specialty.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 2477
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Relational Data Modeling Catalog dimension db id 216
Library dimension (catalog)
Roles linked in library: Fullstack Developer, PHP Backend Developer
-
Relational Database Design Catalog dimension db id 4
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer
-
Search and Content Discovery Catalog dimension db id 356
Library dimension (catalog)
Roles linked in library: Drupal Dev, Sitecore Dev
Aliases — catalog
- query optimization (CANONICAL) primary
- Query optimization (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Performance Optimization Concept
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common in DB/perf job descriptions and interview loops; vendors like PostgreSQL, MySQL, and SQL Server all document EXPLAIN/ANALYZE and indexing as standard tuning practices.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 679
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Performance and Cost Optimization Catalog dimension db id 33
Library dimension (catalog)
Roles linked in library: Data Engineer
-
Performance and Scalability Tuning Catalog dimension db id 11
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Node.js Backend Developer, PHP Backend Developer, Python Backend Developer
Aliases — catalog
- query plans (CANONICAL) primary
- Query plans (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Query Plan
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Query plans are a standard database tuning concept; they appear in JDs for SQL performance work and are exposed by major vendors like PostgreSQL, MySQL, SQL Server, and Oracle via EXPLAIN/EXPLAIN ANALYZE.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 2616
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Relational Database Usage Catalog dimension db id 371
Library dimension (catalog)
Roles linked in library: Go Backend Developer
-
Relational Querying and Transactions Catalog dimension db id 281
Library dimension (catalog)
Roles linked in library: Java Backend Developer
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Anomaly detection (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Ml Monitoring Concept
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common in ML/observability job descriptions and vendor docs (Datadog, Splunk, AWS, Azure) for fraud, monitoring, and alerting; broad market adoption across production systems.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1117
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Data Quality and Reconciliation Catalog dimension db id 27
Library dimension (catalog)
Roles linked in library: Data Engineer
-
Model Monitoring and Drift Detection Catalog dimension db id 45
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Lakehouse (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Architecture
- Sub-category
- Data Platform Architecture
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Lakehouse is increasingly listed in data-platform JDs and vendor docs (Databricks, Snowflake, Microsoft Fabric), but it is not yet as universal as core warehouse or lake skills.
Skill profile (library / DB)
- Skill nature
- PATTERN
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 1
- Sub-category id
- 1026
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Monitoring (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Observability Monitoring
- Confidence
- 0.88
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Monitoring is a standard requirement in most SRE/DevOps job descriptions and is bundled into major platforms like AWS CloudWatch, Datadog, and Prometheus, indicating broad market adoption.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 924
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Observability and Incident Triage Catalog dimension db id 155
Library dimension (catalog)
Roles linked in library: DevOps Engineer
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Library artifacts (this run)
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "1DigitalStack.ai combines AI and deep",
"last_5_words": "brands win on digital shelves."
},
"text": "1DigitalStack.ai combines AI and deep eCommerce data to help global brands grow faster on online marketplaces. Our platforms deliver advanced analytics, actionable intelligence, and media automation \u2014 enabling brands to optimize visibility, efficiency, and sales performance at scale. We partner with India\u2019s top consumer companies \u2014 Unilever, Marico, Coca-Cola, Tata Consumer, Dabur, and Unicharm \u2014 across 125+ marketplaces globally. Backed by leading venture investors and powered by a 220+ member team, we\u2019re in our $5\u201310M growth journey, scaling rapidly across categories and geographies to redefine how brands win on digital shelves.",
"word_count": 84
},
"certifications": [],
"company_name": "1DigitalStack.ai",
"ctc": {
"currency": null,
"max": null,
"min": null,
"period": null,
"raw": "Confidential (based on experience)"
},
"domain": {
"primary": {
"aliases": [
"Tech Consulting",
"IT Solutions"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [],
"experience": {
"max": 7,
"min": 5,
"raw": "5-7 Years"
},
"job_locations": [
{
"aliases": [],
"city": null,
"country": "India",
"state": null,
"work_mode": "remote"
}
],
"role": "Senior Data Engineer",
"role_aliases": [
"Data Engineer",
"Senior Data Engineer",
"Big Data Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 10,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Build and maintain high-throughput,",
"last_5_words": "and platform teams for seamless data delivery."
},
"text": "\u2022 Build and maintain high-throughput, real-time data pipelines using Kafka/Pulsar with Spark,\n\n\u2022 Design fault-tolerant systems with zero-data-loss principles \u2014 checkpointing, replay logic,\n\n\u2022 Implement data observability \u2014 quality checks, SLA alerts, anomaly detection, lineage, and\n\n\u2022 Design and manage Iceberg-based lakehouse tables (Polaris/Gravitino catalogs, schema\n\n\u2022 Build fast OLAP layers using ClickHouse / StarRocks.\n\u2022 Model data across bronze \u2192 silver \u2192 gold layers for downstream teams.\n\u2022 Migrate and modernize legacy pipelines into scalable, distributed workflows.\n\u2022 Orchestrate ETL workloads using Airflow, DBT, Dagster, SQLMesh.\n\u2022 Optimize SQL transformations and distributed execution across Trino/Spark.\n\u2022 Ensure strict security and governance across all data layers \u2014 access control, encryption,\n\n\u2022 Collaborate with backend, analytics, and platform teams for seamless data delivery.",
"word_count": 157
},
{
"bullet_count": 8,
"heading": "Core Technical Skills",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Extremely strong SQL \u2014 window functions,",
"last_5_words": "Airflow, DBT, Dagster, SQLMesh."
},
"text": "\u2022 Extremely strong SQL \u2014 window functions, query planning, optimization.\n\u2022 High comfort working with distributed \u0026 parallel workloads.\n\u2022 Hands-on experience with some-many of these technologies : Apache Spark, Apache Flink,\n\u2022 Advanced experience in Python (preferred) or Java (strong fundamentals).\n\u2022 Strong understanding of Parquet, Apache Iceberg, and Iceberg REST catalogs (Polaris /\n\u2022 Experience with OLAP databases \u2014 ClickHouse / StarRocks.\n\u2022 Experience with semantic layers \u2014 Cube.js or similar.\n\u2022 Strong experience building pipelines with Airflow, DBT, Dagster, SQLMesh.",
"word_count": 104
},
{
"bullet_count": 6,
"heading": "Foundational Strengths",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Solid understanding of data structures",
"last_5_words": "in compute \u0026 storage."
},
"text": "\u2022 Solid understanding of data structures \u0026 algorithms \u2014 sorting, searching, memory models.\n\u2022 Strong grasp of OLTP vs OLAP, indexing, query execution, and storage formats.\n\u2022 Ability to debug distributed systems end-to-end (compute, storage, network, orchestration).\n\u2022 Familiarity with cloud environments, containerization (Docker), and monitoring.\n\u2022 Experience with large-scale data \u2014 high throughput, billions of rows, large parallel workloads.\n\u2022 Awareness of cost optimization in compute \u0026 storage.",
"word_count": 90
},
{
"bullet_count": 2,
"heading": "Good to Have",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Experience with emerging stream processors",
"last_5_words": "or cloud-native big-data stacks."
},
"text": "\u2022 Experience with emerging stream processors \u2014 Dagster, RisingWave, Arroyo.\n\u2022 Kubernetes, Terraform, or cloud-native big-data stacks.",
"word_count": 24
},
{
"bullet_count": 4,
"heading": "Mindset",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Strong ownership \u2014 takes systems",
"last_5_words": "responsibility as the platform scales."
},
"text": "\u2022 Strong ownership \u2014 takes systems from design \u2192 build \u2192 monitor.\n\u2022 Self-driven, independent, and comfortable making technical decisions.\n\u2022 High attention to reliability, data accuracy, and operational excellence.\n\u2022 Naturally grows into broader technical responsibility as the platform scales.",
"word_count": 40
}
],
"urls": [
{
"type": "website",
"url": "http://www.1digitalstack.ai"
}
]
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Kafka"
},
{
"is_primary": true,
"skill_name": "Pulsar"
},
{
"is_primary": true,
"skill_name": "Spark"
},
{
"is_primary": true,
"skill_name": "Apache Spark"
},
{
"is_primary": false,
"skill_name": "Apache Flink"
},
{
"is_primary": true,
"skill_name": "Apache Iceberg"
},
{
"is_primary": false,
"skill_name": "Polaris"
},
{
"is_primary": false,
"skill_name": "Gravitino"
},
{
"is_primary": true,
"skill_name": "ClickHouse"
},
{
"is_primary": true,
"skill_name": "StarRocks"
},
{
"is_primary": true,
"skill_name": "Airflow"
},
{
"is_primary": true,
"skill_name": "dbt"
},
{
"is_primary": true,
"skill_name": "Dagster"
},
{
"is_primary": true,
"skill_name": "SQLMesh"
},
{
"is_primary": true,
"skill_name": "Trino"
},
{
"is_primary": true,
"skill_name": "SQL"
},
{
"is_primary": true,
"skill_name": "Python"
},
{
"is_primary": false,
"skill_name": "Java"
},
{
"is_primary": true,
"skill_name": "Parquet"
},
{
"is_primary": false,
"skill_name": "Cube.js"
},
{
"is_primary": false,
"skill_name": "Docker"
},
{
"is_primary": false,
"skill_name": "Kubernetes"
},
{
"is_primary": false,
"skill_name": "Terraform"
},
{
"is_primary": true,
"skill_name": "Distributed Systems"
},
{
"is_primary": false,
"skill_name": "OLTP"
},
{
"is_primary": true,
"skill_name": "OLAP"
},
{
"is_primary": false,
"skill_name": "Indexing"
},
{
"is_primary": false,
"skill_name": "Query Optimization"
},
{
"is_primary": false,
"skill_name": "Query Planning"
},
{
"is_primary": false,
"skill_name": "Window Functions"
},
{
"is_primary": false,
"skill_name": "Checkpointing"
},
{
"is_primary": false,
"skill_name": "Replay Logic"
},
{
"is_primary": false,
"skill_name": "Data Lineage"
},
{
"is_primary": false,
"skill_name": "Anomaly Detection"
},
{
"is_primary": false,
"skill_name": "Data Observability"
},
{
"is_primary": true,
"skill_name": "ETL"
},
{
"is_primary": true,
"skill_name": "Data Pipelines"
},
{
"is_primary": true,
"skill_name": "Lakehouse"
},
{
"is_primary": false,
"skill_name": "Semantic Layers"
},
{
"is_primary": false,
"skill_name": "Monitoring"
},
{
"is_primary": false,
"skill_name": "Containerization"
},
{
"is_primary": false,
"skill_name": "Encryption"
},
{
"is_primary": false,
"skill_name": "Access Control"
},
{
"is_primary": false,
"skill_name": "Distributed Execution"
},
{
"is_primary": false,
"skill_name": "Parallel Workloads"
},
{
"is_primary": false,
"skill_name": "Query Execution"
},
{
"is_primary": false,
"skill_name": "Storage Formats"
},
{
"is_primary": false,
"skill_name": "Big Data"
},
{
"is_primary": false,
"skill_name": "RisingWave"
},
{
"is_primary": false,
"skill_name": "Arroyo"
}
],
"jd_role": {
"display_name": "Senior Data Engineer",
"rationale": null,
"role_aliases": [
"Data Engineer",
"Senior Data Engineer",
"Big Data Engineer"
],
"role_archetype": "Data",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "1DigitalStack.ai combines AI and deep",
"last_5_words": "brands win on digital shelves."
},
"text": "1DigitalStack.ai combines AI and deep eCommerce data to help global brands grow faster on online marketplaces. Our platforms deliver advanced analytics, actionable intelligence, and media automation \u2014 enabling brands to optimize visibility, efficiency, and sales performance at scale. We partner with India\u2019s top consumer companies \u2014 Unilever, Marico, Coca-Cola, Tata Consumer, Dabur, and Unicharm \u2014 across 125+ marketplaces globally. Backed by leading venture investors and powered by a 220+ member team, we\u2019re in our $5\u201310M growth journey, scaling rapidly across categories and geographies to redefine how brands win on digital shelves.",
"word_count": 84
},
"certifications": [],
"company_name": "1DigitalStack.ai",
"ctc": {
"currency": null,
"max": null,
"min": null,
"period": null,
"raw": "Confidential (based on experience)"
},
"domain": {
"primary": {
"aliases": [
"Tech Consulting",
"IT Solutions"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [],
"experience": {
"max": 7,
"min": 5,
"raw": "5-7 Years"
},
"job_locations": [
{
"aliases": [],
"city": null,
"country": "India",
"state": null,
"work_mode": "remote"
}
],
"role": "Senior Data Engineer",
"role_aliases": [
"Data Engineer",
"Senior Data Engineer",
"Big Data Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 10,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Build and maintain high-throughput,",
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API 2 — extract-details
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}
API 3 — final-role-output
{}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.