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.
- Clear communication with architects, backend / AI engineers, and product.

Nice-to-Have

- ERP / financial source data (NetSuite, SAP, Oracle, Workday) and an understanding of how financial statements are built and reconciled.
- Multi-cloud and customer-hosted deployment experience.
- FinTech or financial-services domain background, and exposure to SOC 2 / audit expectations for data.
- Experience generating synthetic / golden datasets for platform development and testing.
- Familiarity with feeding ML / LLM systems from a curated data layer.

Candidate responsibilities

- Data Model & Architecture (30%)
Own the architecture of the unified data model source systems map into
Design canonical financial & operational entities, hierarchies, dimensions, and time periods
Co-design with the platform architect so the model generalizes across companies and domains




Make the calls that keep onboarding a configuration change, not a code fork
- Source Integration & Mapping (25%)
Design the source-to-model mapping framework and connector strategy
Cover GL / EDW, HRIS, payroll, AP / procurement, equity admin, accrual & prepaid workpapers, plan / forecast, and event logs
Define how recent sources onboard with minimal bespoke work and maximum reuse
Own the synthetic / golden-data strategy for pre-integration development and testing
- Lineage, Reconciliation & Quality (20%)
Establish lineage capture, data contracts, and reconciliation standards
Guarantee every figure traces back to source in one or two hops
Ensure idempotent, reproducible pipelines — same inputs, same outputs
Define the quality gates the rest of the platform depends on
- Orchestration & Platform (10%)
Own the transformation & orchestration platform (dbt-style transforms, workflow orchestration)
Own the curated stores and the close-cycle refresh / scheduling model
Own performance, reliability, and cost of the data tier
- Multi-Cloud & Multi-Tenant (8%)
Keep the data layer portable across AWS, Azure, and GCP
Make it deployable into a customer's own cloud with strong tenant isolation and no egress
Partner with platform engineering on the data-tier deployment / isolation model
- Technical Leadership (7%)
Lead and mentor the offshore data-engineering team
Set patterns, run design reviews, and raise the bar
Partner with architect, backend, and AI teams without becoming a bottleneck

📌 Data Engineer (Hyderabad)
🏢 Grid Dynamics
📍 Hyderabad