Principal Data Engineer

<div><div style="padding: 10px 0px;border: 1px solid transparent;"><div style="font-size:16px;word-wrap: break-word;"><h2 style="font-size: 1em; margin: 0px">What you’ll do:</h2> </div><div><p>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<strong> </strong>hire<strong> Specialist/ Principal Data Engineer </strong>in<strong> Pune, India.</strong> In Eaton, making our work exciting, engaging, meaningful; ensuring safety, health, wellness; and being a model of inclusion &amp; diversity are already embedded in <strong>who we are - it’s in our values, part of our vision, and our clearly </strong>defined aspirational goals. This exciting role offers opportunity to:</p><ul><li>The principal Data Engineer is a pivotal role within the Finance Data Hub and the Enterprise Data platform, focused on establishing standards, building frameworks, and elevating engineering capabilities across data organization.</li><li>Rather than solely delivering features, this position defines and codifies principles and practices for building, testing, deploying, monitoring, and governing data pipelines in a modern DataOps and data mesh environment.</li><li>The impact extends beyond functional pipelines, creating a reusable foundation that empowers every data team member to deliver high-quality data products efficiently and confidently.</li><li>The ideal candidate brings deep technical expertise, a platform engineering mindset, and strong leadership to drive adoption of new standards, with a forward-looking approach to GenAI-augmented data engineering.</li><li>This role is directly accountable for establishing, documenting, and driving adoption of six foundational engineering frameworks that will define the data engineering operating model for the Finance Data Hub:</li><li>This role will define and codify leading practices across the full data lifecycle — including</li><li>DataOps framework for CI/CD-driven pipeline deployment, automated unit testing,</li><li>Data Quality framework with data contract testing, schema validation, and anomaly detection,</li><li>Data Observability standard for end-to-end lineage tracking, freshness monitoring, and incident response,</li><li>Data Modeling standard aligned to medallion or dimensional patterns with naming conventions and style guides,</li><li>Data Governance and Access Control framework covering classification, masking, and role-based access, and</li><li>Pipeline Design Pattern library of reusable, idempotent, and testable ELT/ETL templates.</li></ul></div></div><div style="padding: 10px 0px;border: 1px solid transparent;"><div style="font-size:16px;word-wrap: break-word;"><h2 style="font-size: 1em; margin: 0px">Qualifications:</h2> </div><div><p>Requirement :</p><ul><li>B E/M.Tech in Electrical/Electronics/Computer Science</li><li>12+ years</li><li>End-to-end delivery of production data pipelines at enterprise scale: ingestion, transformation, orchestration, and serving layers. Strong SQL and Python proficiency</li><li>Experience with both batch and streaming paradigms</li><li>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</li><li>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</li><li>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</li><li>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</li><li>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</li><li>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</li><li>Has implemented data quality monitoring frameworks and observability instrumentation in production environments</li><li>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</li><li>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</li><li>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.</li></ul></div></div><div style="padding: 10px 0px;border: 1px solid transparent;"><div style="font-size:16px;word-wrap: break-word;"><h2 style="font-size: 1em; margin: 0px">Skills:</h2> </div><div><p>1. Framework Authorship &amp; Adoption Leadership</p><p>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.</p><p>Conduct framework enablement sessions, workshops, and pair-programming to drive active adoption — not just publication — across the engineering team.</p><p>Define conformance criteria and lightweight review checkpoints so that new pipeline work is assessed against framework standards before promotion to production.</p><p>Act as the technical authority and tiebreaker on engineering design decisions — establishing consistent patterns while preserving pragmatic flexibility where needed.</p><p>2. DataOps &amp; CI/CD Pipeline Engineering</p><p>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.</p><p>Establish automated unit testing patterns — including test coverage standards and coverage reporting.</p><p>3. Data Quality &amp; Observability Engineering</p><p>Implement data contract frameworks at ingestion, transformation, and consumption boundaries — defining schemas, SLOs, and acceptable value ranges as code.</p><p>Build reusable data quality monitoring templates — parameterizable and composable across data products.</p><p>Instrument pipelines with observability metadata: lineage, runtime metrics, freshness timestamps, and row count deltas — surfaced into operational dashboards.</p><p>Design and test the incident response workflow for data quality breaches: automated alerting, quarantine patterns, stakeholder notification, and self-healing logic where feasible.</p><p>4. Snowflake Platform &amp; Access Control Engineering</p><p>Design and implement scalable RBAC models in Snowflake — covering functional roles, object ownership hierarchies, and data product consumer roles.</p><p>Build row-level security (RLS) frameworks using Snowflake row access policies — creating reusable, metadata-driven policy templates that can be applied consistently across Finance data products.</p><p>Define and implement dynamic data masking policies aligned to the data classification taxonomy — ensuring sensitive financial data is protected at the platform layer, not just the application layer.</p><p>Govern Snowflake resource utilization: warehouse sizing standards, query optimization guidelines, and cost attribution tagging by domain or product.</p><p>5. GenAI-Augmented Data Engineering</p><p>Champion the exploration and adoption of GenAI tooling to amplify data engineering productivity — including AI-assisted SQL/python code generation, automated documentation, and intelligent pipeline debugging.</p><p>Prototype and evaluate LLM-powered data engineering assistants: natural language to SQL interfaces, automated data contract generation, and AI-driven anomaly root cause analysis.</p><p>Define guardrails and governance standards for GenAI use in data engineering workflows — covering code review requirements, hallucination risk in data contexts, and audit traceability.</p><p>Share findings and tooling recommendations with the wider data engineering community through internal demos, documentation, and engineering blog posts.</p><p>6. Modernization &amp; Delivery Velocity</p><p>Identify and eliminate sources of engineering friction — legacy patterns, manual deployment steps, inconsistent environments — and replace with automated, standards-driven equivalents.</p><p>Measure and report on delivery cycle time improvements attributable to framework adoption: pipeline build time, time to production, defect escape rate, and time to recovery.</p><p>Lead or contribute to data engineering modernization initiatives: migrating legacy ETL workloads, re-platforming to Snowflake, and adopting modern orchestration patterns.</p></div></div></div>