paycor
Senior Software Engineer- Big Data & MCP, Data Foundations
RevSpring Inc
Portland, USA
Full-time
Senior Level
Onsite
Discovered 1 weeks ago
Free
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- Collaborate and Innovate: Partner with product managers, data engineers, and business leaders to translate complex product and data requirements into scalable, reliable data pipelines and the search experiences they power.
- Architect Data Pipelines: Design, build, and optimize large-scale distributed batch and streaming pipelines (using Apache Airflow, Apache Beam/Dataflow, and DBTon BigQuery) to ingest, model, and transform high-volume healthcare data into clean, well-tested, query-ready datasets and search indices.
- Build Data Models & Backend Services: Develop resilient Python services and DBT models that power data delivery and self-service analytics, including Model Context Protocol (MCP) servers that expose curated data and tooling to downstream and AI consumers, and integrate with external REST/SOAP APIs and third-party data sources.
- Optimize Data & Search Performance: Deeply tune pipeline throughput, data warehouse performance, and search indexing — optimizing BigQuery cost and query performance and Elasticsearch index design to ensure data freshness, relevance, and scalability across high-volume datasets.
- Drive Engineering Excellence: Write clean, maintainable, well-tested code and lead by example through rigorous code reviews, architectural and data-modeling design discussions, and mentoring, driving a culture of high-quality software and trustworthy data.
- Pioneer New Technologies: Stay at the forefront of modern data engineering, the analytics-engineering ecosystem (e.g., DBT, BigQuery), and information retrieval, proactively applying these advancements to strengthen our data platform and the products it powers.
- Data Engineering: Proven experience designing and orchestrating large-scale ETL/ELT pipelines using Apache Beam/Google Cloud Dataflow (or similar), and DBT, built on modern cloud data warehouses. BigQuery experience is a plus.
- Databases & SQL: 4+ years of experience working with relational databases and analytical data warehouses, with deep, advanced SQL skills and solid data-modeling fundamentals (e.g., dimensional and normalized modeling).
- Search & Indexing: Working experience with search indexing and Elasticsearch, including index management, mappings, and building and maintaining search indices from pipeline output. Familiarity with hybrid (BM25 + semantic/vector) search is a plus.
- Backend & Data Services: Experience building scalable Python services and high-performance data APIs, including developing Model Context Protocol (MCP) servers that expose data and tooling to downstream and AI consumers.
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