Principal Data Engineer

What you'll do: 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 tohire Principal Data Engineer in Pune, India. In Nimbus Data Systems, making our work exciting, engaging, meaningful; ensuring safety, health, wellness; and being a model of inclusion & diversity are already embedded in who we are - it's in our values, part of our vision, and our clearly defined aspirational goals. This exciting role offers opportunity to: 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. 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. 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. 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. 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: This role will define and codify leading practices across the full data lifecycle - including DataOps framework for CI/CD-driven pipeline deployment, automated unit testing, Data Quality framework with data contract testing, schema validation, and anomaly detection, Data Observability standard for end-to-end lineage tracking, freshness monitoring, and incident response, Data Modeling standard aligned to medallion or dimensional patterns with naming conventions and style guides, Data Governance and Access Control framework covering classification, masking, and role-based access, and Pipeline Design Pattern library of reusable, idempotent, and testable ELT/ETL templates. Qualifications: Requirement : B E/M.Tech in Electrical/Electronics/Computer Science 10+ 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. Skills: 1. Framework Authorship & Adoption Leadership 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 pip