Data Engineer

Role Context

Hands-on senior data engineer responsible for building and operating reliable, scalable ETL/ELT pipelines across multi-source systems. Ensures data integrity, synchronization, and quality, while supporting analytics and lightweight AI/ML consumption needs.

Note: Apply only if you are available for Face to Face Interview at Pune Location on 10th Jan 2026

Key Responsibilities

Build and enhance ETL/ELT pipelines (batch and near real-time) across multiple sources.

Implement transformations aligned to target schemas and data models.

Handle incremental loads, CDC patterns, reprocessing, and dependency orchestration.

Develop data quality checks, reconciliation logic, and pipeline observability.

Contribute to performance tuning (SQL, partitioning strategies, job optimization).

Work with QA to define and validate test scenarios and acceptance criteria.

Collaborate with Technical BA and Modellers to ensure correctness of mappings and semantics.

Support production operations: incident triage, root cause analysis, and fixes.

Must Have

6+ years in data engineering / ETL development.

Strong SQL (complex joins, window functions, tuning fundamentals).

Experience with an ETL tool or transformation framework (dbt / ADF / Glue / Informatica / Talend / DataStage).

Orchestration exposure (Airflow or equivalent).

Python scripting for automation, validation, and utility development.

Understanding of data integrity, dependencies, SCDs, and incremental load strategies.

Delivery/DevOps fundamentals: Git PR workflow, CI/CD awareness, environment promotion discipline.

Observability basics: logs/metrics/alerts; ability to troubleshoot pipelines.

Non-technical: ownership, documentation discipline, good communication with cross-functional teams.

PySpark/Spark SQL implementation experience

Good To Have

Kafka/event streaming exposure.

Marketing/customer data exposure.

Data testing frameworks (Great Expectations/dbt tests).

Strong stakeholder handling during incidents and release cycles.