## What youu2019ll do:nnIf you desire to be part of something special, to be part of a winning team, to be part of a fun team u2013 winning is fun. We are looking forward to hire Sr. Data Engineer in Pune, India. In Eaton, making our work exciting, engaging, meaningful; ensuring safety, health, wellness; and being a model of inclusion u0026 diversity are already embedded in who we are - itu2019s in our values, part of our vision, and our clearly defined aspirational goals. This exciting role offers opportunity to:nn * The Senior 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.n * Rather than solely delivering features, this position defines and codifies principles and practices for building, testing, deploying, monitoring, and governing data pipelines in a up-to-date DataOps and data mesh environment.n * 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.n * The ideal candidate brings deep technical expertise, a platform engineering mindset, and strong leadership to drive adoption of current standards, with a forward-looking approach to GenAI-augmented data engineering.n * 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:n * This role will define and codify leading practices across the full data lifecycle u2014 includingn * DataOps framework for CI/CD-driven pipeline deployment, automated unit testing,n * Data Quality framework with data contract testing, schema validation, and anomaly detection,n * Data Observability standard for end-to-end lineage tracking, freshness monitoring, and incident response,n * Data Modeling standard aligned to medallion or dimensional patterns with naming conventions and style guides,n * Data Governance and Access Control framework covering classification, masking, and role-based access, andn * Pipeline Design Pattern library of reusable, idempotent, and testable ELT/ETL templates.nnnn## Qualifications:nnRequirement :nn * B E/M.Tech in Electrical/Electronics/Computer Sciencen * 10+ yearsn * End-to-end delivery of production data pipelines at enterprise scale: ingestion, transformation, orchestration, and serving layers. Strong SQL and Python proficiencyn * Experience with both batch and streaming paradigmsn * Technical leadership in a cross-functional environment u2014 setting standards, mentoring engineers, conducting design reviews, and influencing engineering direction without necessarily holding a direct management titlen * 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 plusn * 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 workloadsn * Hands-on experience building transformation tools u2014 models, tests, macros, packages, sources, and exposures. Coalesce experience or familiarity is an advantage. Understanding of DAG-based transformation orchestrationn * 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 testsn * 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 enforcementn * Has implemented data quality monitoring frameworks and observability instrumentation in production environmentsn * 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 standardsn * Has led or meaningfully contributed to a data engineering modernization initiative u2014 re-platforming, cycle time reduction, or adoption of modern tooling. Can articulate before/after outcomes with metricsn * Has experimented with or productionised GenAI tools to enhance data engineering workflows u2014 AI code assistants, LLM-powered documentation, natural language querying, or AI-driven anomaly analysis.nnnn## Skills:nn1\. Framework Authorship u0026 Adoption LeadershipnnDesign, document, and version-control all six engineering frameworks in a central standards repository (GitHub), ensuring they are discoverable, living documents with clear change governance.nnConduct framework enablement sessions, workshops, and pair-programming to drive active adoption u2014 not just publication u2014 across the engineering team.nnDefine conformance criteria and lightweight review checkpoints so that recent pipeline work is assessed against framework standards before promotion to production.nnAct as the technical authority and tiebreaker on engineering design decisions u2014 establishing consistent patterns while preserving pragmatic flexibility where needed.nn2\. DataOps u0026 CI/CD Pipeline EngineeringnnDesign and implement CI/CD pipelines for data engineering workloads using GitHub Actions or equivalent u2014 covering lint, unit test, schema validation,



and environment promotion stages.nnEstablish automated unit testing patterns u2014 including test coverage standards and coverage reporting.nn3\. Data Quality u0026 Observability EngineeringnnImplement data contract frameworks at ingestion, transformation, and consumption boundaries u2014 defining schemas, SLOs, and acceptable value ranges as code.nnBuild reusable data quality monitoring templates u2014 parameterizable and composable across data products.nnInstrument pipelines with observability metadata: lineage, runtime metrics, freshness timestamps, and row count deltas u2014 surfaced into operational dashboards.nnDesign and test the incident response workflow for data quality breaches: automated alerting, quarantine patterns, stakeholder notification, and self-healing logic where feasible.nn4\. Snowflake Platform u0026 Access Control EngineeringnnDesign and implement scalable RBAC models in Snowflake u2014 covering functional roles, object ownership hierarchies, and data product consumer roles.nnBuild row-level security (RLS) frameworks using Snowflake row access policies u2014 creating reusable, metadata-driven policy templates that can be applied consistently across Finance data products.nnDefine and implement dynamic data masking policies aligned to the data classification taxonomy u2014 ensuring sensitive financial data is protected at the platform layer, not just the application layer.nnGovern Snowflake resource utilization: warehouse sizing standards, query optimization guidelines, and cost attribution tagging by domain or product.nn5\. GenAI-Augmented Data EngineeringnnChampion the exploration and adoption of GenAI tooling to amplify data engineering productivity u2014 including AI-assisted SQL/python code generation, automated documentation, and intelligent pipeline debugging.nnPrototype and evaluate LLM-powered data engineering assistants: natural language to SQL interfaces, automated data contract generation, and AI-driven anomaly root cause analysis.nnDefine guardrails and governance standards for GenAI use in data engineering workflows u2014 covering code review requirements, hallucination risk in data contexts, and audit traceability.nnShare findings and tooling recommendations with the wider data engineering community through internal demos, documentation, and engineering blog posts.nn6\. Modernization u0026 Delivery VelocitynnIdentify and eliminate sources of engineering friction u2014 legacy patterns, manual deployment steps, inconsistent environments u2014 and replace with automated, standards-driven equivalents.nnMeasure and report on delivery cycle time improvements attributable to framework adoption: pipeline build time, time to production, defect escape rate, and time to recovery.nnLead or contribute to data engineering modernization initiatives: migrating legacy ETL workloads, re-platforming to Snowflake, and adopting modern orchestration patterns.n

📌 Sr. Data Engineer (Pune)
🏢 Nameless
📍 Pune