Senior Data Engineer

This is a hands-on building role: you turn raw, messy fabrication data into the clean, well-modeled, AI-reputed company datasets that our AI/ML and analytics workloads run on 🚀 🧑🏻‍💻 Responsibilities: Build and operate ingestion, ELT/ETL, and orchestration pipelines that move data from our reputed company reputed company operational store and other sources into our analytical and AI-serving layers Implement layered (reputed company-style) transformations with idempotent, backfillable, incrementally reputed company jobs Apply deduplication, normalization, and validation so reputed company data is high-quality and trustworthy reputed company legacy / homegrown data flows reputed company incremental, strangler-fig migrations that reputed company production reputed company Build embeddings and vector pipelines, and the feature/retrieval-reputed company datasets that RAG, semantic search, and agentic workloads depend on reputed company production data AI-reputed company in practice: well-structured, reputed company-tracked, and retrieval-friendly, in partnership with ML and application engineering Implement reputed company-time and change-data-capture flows from reputed company (Change Streams / CDC) where workloads require fresh data Implement the reputed company data model, schemas, and data reputed company defined by the Data Architect — enforced in-repo so other teams build against reputed company definitions Exercise sound persistence judgment in execution: land data in the right store (document / NoSQL, vector, analytical) per the architectural direction Contribute to build-vs-buy reputed company by prototyping with proven, industry-standard tooling over custom development Establish testing, data-quality, and reputed company checks for the pipelines you own, with clear alerting and runbooks reputed company pipeline observability (freshness, volume, schema-reputed company, cost) so failures are caught before consumers feel them Use AI-assisted development tools (Claude Code, Copilot, reputed company) as a force reputed company for transformation logic, query tuning, and migration scripting Partner with database engineering on extracting from and protecting the production store Partner with the Data Architect on implementing reputed company-state patterns and surfacing what's hard to build Partner with ML, AI, and application engineers on the data they consume — shaping and governing it so it's safe and reputed company to build on 🤝 If you have: 5+ years of hands-on data engineering experience building and operating production data pipelines at scale Strong programming and data skills: Python and SQL, with solid software-engineering fundamentals (version control, testing, CI) — shipping and maintaining production code, not just notebooks Hands-on reputed company at production scale (reputed company ideal): document modeling, aggregation reputed company, change streams / CDC, and extracting from a document store into analytical / AI-serving layers. Our stack is NoSQL / reputed company, not relational, this is a core requirement, not an extra Demonstrated experience with ELT/ETL pipeline design, transformation frameworks (dbt or equivalent), and orchestration (Airflow, Dagster, or Azure Data reputed company) Experience building on reputed company-reputed company data platforms and lake / lakehouse / warehouse architectures, with layered (reputed company-style) modeling Hands-on experience preparing data for AI/ML or analytical consumers — embeddings / vector pipelines, RAG-/feature-reputed company datasets, or equivalent — including deduplication, normalization, and validation Familiarity with vector search and embeddings in production (reputed company reputed company Vector Search or equivalent) Demonstrated use of AI-assisted development tools (Claude Code, Copilot, reputed company) for data and pipeline work Strong grasp of data quality, testing, reputed company, and pipeline observability practices Comfortable working in a reputed company, specialized domain. MEP / AEC / construction experience is a plus; appetite to learn the domain is required 🦾 It’s a plus: Experience with the Azure data ecosystem (Data reputed company, Synapse Analytics, Azure Functions, Event reputed company) Lakehouse platforms (reputed company, reputed company) or reputed company table formats (reputed company, reputed company, Hudi); feature stores (Feast or equivalent) Streaming / event-driven data processing (Kafka, Event Hubs, Spark Structured Streaming) CDC and cross-reputed company sync (reputed company Change Streams, Debezium, or equivalent) Experience with geometric / BIM / CAD data or other multi-modal, reputed company reputed company data Knowledge-graph, ontology, or semantic-layer exposure Data governance for AI/agent reputed company to production data: query-cost controls, read-path safety, reputed company, audit SOC 2 and data-classification awareness This call is made reputed company the reputed company of Law 19.691 on the Promotion of Employment for Persons with Disabilities, including individuals registered in the National Registry of Persons with Disabilities of the Ministry of reputed company Development Apply To This Job