Note:

Years of Experience - 7+ Years

Job Location - Chennai

Notice Period - Immediate to 15 days only

Final Level discussion Face to Face Mandatory

Technical Stack

Data & Warehouses
- Snowflake, BigQuery, PostgreSQL
- Dimensional & canonical data modeling
- Advanced SQL (window functions, large joins, CTEs)
- Warehouse internals, partitioning & performance tuning

Transformation & Orchestration
- dbt (or equivalent) at scale
- Airflow / Dagster / Prefect
- Python
- Incremental / CDC-aware modeling

Ingestion & Integration
- CDC / Fivetran-style ingestion
- ERP / financial sources (NetSuite, SAP, Oracle, Workday)
- Data contracts & schema-evolution handling
- Lineage tooling (OpenLineage, dbt exposures)

Quality & Cloud
- dbt tests / Great Expectations
- AWS / Azure / GCP
- Docker, containerized customer-hosted deployment
- Multi-tenant isolation patterns

Required Qualifications

Core
- 10+ years in data engineering, with significant time at Staff+ / lead level owning system-level data architecture.




- Designed canonical or common data models and large-scale transformation and orchestration systems that served multiple downstream consumers.
- A bias toward correctness and traceability "every number ties to source" is a requirement you hold the line on.

Data Engineering Depth
- Expert SQL and dbt(-style) modeling, including performance tuning on large datasets.
- Production orchestration (Airflow / Dagster / Prefect) with idempotent, reproducible pipelines.
- Warehouses — Snowflake / BigQuery / PostgreSQL internals and cost/performance trade-offs.
- Deep command of lineage, data contracts, data quality, and schema evolution.

Leadership
- Track record leading and mentoring data engineers, ideally in a distributed / offshore setting.
- Strong design-review and standard-setting instincts; able to make and defend build-vs-configure calls.
- Transparent communication with architects, backend / AI engineers, and product.

Nice-to-Have
- ERP / financial source data (NetSui

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