Own ETL architecture patterns, reusable frameworks, and standardization for multi-source data integration. Ensures scalability, maintainability, and governance across ETL delivery, aligning to product roadmap and data model strategy.

Key Responsibilities

Define reference architecture for ETL/ELT pipelines: ingestion → transform → serving layers.

Establish standards for metadata/config-driven ETL, naming, error handling, and replay.

Design scalable patterns for CDC, incremental processing, backfills, and dependency orchestration.

Define CI/CD patterns for ETL and govern environment promotions.

Define observability standards: logs, metrics, lineage, data quality monitoring, SLAs.

Review designs for performance, cost, security, and operational readiness.

Align ETL patterns with data modelling and downstream analytics/ML needs.

Must Have

8–12 years in data engineering with strong ETL architecture ownership.

Expertise across ETL tools/frameworks (dbt + orchestration, ADF/Glue/Informatica/Talend etc.).

Strong SQL performance and warehouse optimization knowledge.

Airflow (or equivalent) architecture-level orchestration design.

Python capability for framework/tooling design and automation.

Strong knowledge of cloud data architecture (AWS/Azure/GCP).

Delivery engineering maturity: CI/CD, versioning, release discipline, rollback strategies.

Non-technical: strong documentation, workshop facilitation, ability to influence teams.

Good To Have

Experience with event streaming architectures (Kafka) and lakehouse patterns.

Exposure to data catalog/lineage tooling and governance practices.

Familiarity with ML pipeline needs (feature readiness, time semantics).

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