Principal Data Engineer

If you desire to be part of something special, to be part of a winning team, to be part of a fun team - winning is fun. We are looking forward to hire Specialist Data Engineer in Pune, India. B E/M.Tech in Electrical/Electronics/Computer Science 12+ years End-to-end delivery of production data pipelines at enterprise scale: ingestion, transformation, orchestration, and serving layers. Strong SQL and Python proficiency Experience with both batch and streaming paradigms Technical leadership in a cross-functional environment — setting standards, mentoring engineers, conducting design reviews, and influencing engineering direction without necessarily holding a direct management title Deep hands-on Snowflake expertise: data sharing, zero-copy cloning, dynamic tables, streams and tasks, RBAC design, row access policies, dynamic masking, warehouse sizing, and query optimization. Snowflake certification is a strong plus Proficient with GitHub for version control, pull request workflows, and GitHub Actions for CI/CD automation. Experience designing branching strategies and automated test/deploy pipelines for data workloads Hands-on experience building transformation tools — models, tests, macros, packages, sources, and exposures. Coalesce experience or familiarity is an advantage. Understanding of DAG-based transformation orchestration Has built or adopted reusable automated unit testing frameworks for data pipelines or transformation models. Understands test pyramid concepts in a data context: unit, integration, and contract tests Has designed and implemented RLS frameworks at the platform layer (e.g., Snowflake row access policies). Understands the intersection of data governance policy and platform enforcement Has implemented data quality monitoring frameworks and observability instrumentation in production environments Strong grasp of medallion architecture (Bronze/Silver/Gold), dimensional modeling (star schema, SCD types), and modern lakehouse/warehouse modeling patterns. Has published or enforced modeling standards Has led or meaningfully contributed to a data engineering modernization initiative — re-platforming, cycle time reduction, or adoption of modern tooling. Can articulate before/after outcomes with metrics Has experimented with or productionised GenAI tools to enhance data engineering workflows — AI code assistants, LLM-powered documentation, natural language querying, or AI-driven anomaly analysis. 1. Design, document, and version-control all six engineering frameworks in a central standards repository (GitHub), ensuring they are discoverable, living documents with clear change governance. Conduct framework enablement sessions, workshops, and pair-programming to drive active adoption — not just publication — across the engineering team. Define conformance criteria and lightweight review checkpoints so that new pipeline work is assessed against framework standards before promotion to production. Act as the technical authority and tiebreaker on engineering design decisions — establishing consistent patterns while preserving pragmatic flexibility where needed. 2. DataOps & CI/CD Pipeline Engineering Design and implement CI/CD pipelines for data engineering workloads using GitHub Actions or equivalent — covering lint, unit test, schema validation, and environment promotion stages. Establish automated unit testing patterns — including test coverage standards and coverage reporting. 3. Data Quality & Observability Engineering Implement data contract frameworks at ingestion, transformation, and consumption boundaries — defining schemas, SLOs, and acceptable value ranges as code. Build reusable data quality monitoring templates — parameterizable and composable across data products. Instrument pipelines with observability metadata: lineage, runtime metrics, freshness timestamps, and row count deltas — surfaced into operational dashboards. Design and test the incident response workflow for data quality breaches: automated alerting, quarantine patterns, stakeholder notification, and self-healing logic where feasible. 4.