Sr Data Engineer

· Partner with business and finance stakeholders to frame data problems and deliver end-to-end solutions.
· Design, develop, and maintain scalable data pipelines and ELT processes using GCP and BigQuery.
· Build and maintain layered data platforms using medallion architecture patterns, including Bronze, Silver, and Gold layers.
· Create curated data products, analytical datasets, and semantic models that support reporting, analytics, and decision-making.
· Integrate data from enterprise applications, finance systems, operational databases, APIs, and other source platforms.
· Conduct data profiling, source-to-target mapping, reconciliation, cataloging, and quality assessments.
· Translate stakeholder requirements into practical, scalable data solutions.
· Document designs, data flows, lineage, transformation logic, and operating procedures.
· Support, troubleshoot, and enhance data pipelines and interim legacy systems.
· Apply standards for security, performance, scalability, testing, monitoring, and governance.
· Contribute to sprint planning, backlog refinement, estimation, demos, and continuous improvement.
· Use automation, generative AI, and agentic tools to improve engineering productivity and delivery processes.
· Apply AI across the data engineering lifecycle, including discovery, mapping, SQL and pipeline generation, testing, quality rules, documentation, and lineage.
· Build AI-native data solutions such as natural-language data access, data agents, RAG/context layers, embeddings, and semantic search.
· Evaluate and operationalize AI tooling, including agent orchestration, tool/function calling, MCP servers, prompt and context engineering, evaluations, and guardrails.
· Curate metadata, definitions, and context that make data usable by people and AI agents.
· Own delivery from design through deployment, adoption, and ongoing enhancement, with accountability for business value and reliable operations.