Data Engineer

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 new 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