Data Engineer, Rimes Technologies

• 1-3 years in data engineering or analytics engineering with end‑to‑end pipeline delivery in production
• Proficiency in Python & PySpark for distributed data processing
• Strong SQL for analytical and transformation logic
• Data modeling skills for both analytics and operational use cases
• Experience with data ingestion from APIs, databases, external feeds, and real-time sources
• Solid grasp of data quality, testing, observability, lineage, and governance practices
• Comfort working with large datasets and distributed compute using modern ELT patterns

Desirable

• We’re looking for a Data Engineer to own data onboarding and build scalable, reliable data pipelines that power analytics, operational workflows, and data‑driven decisions across Rimes
• You’ll work closely with data producers, analysts, and product teams to ingest, transform, and operationalize data across Palantir Foundry and Databricks as our two target data platforms
• Note: Experience with Palantir Foundry or Databricks is a strong plus but not required. If you bring solid data engineering fundamentals in Python/PySpark, SQL, and modern ELT patterns, we’ll support a fast ramp‑up on both platforms
• Ingest & onboard datasets from internal systems, APIs, databases, files, external providers, and real‑time feeds
• Build and operate scalable ETL/ELT pipelines using Python, PySpark, SQL, and Foundry pipeline tooling; schedule and automate batch/stream refreshes
• Model and operationalize data (e.g., defining entities/relationships) to support analytics and operational applications in collaboration with domain experts
• Ensure trust in data through testing, data quality checks, observability/alerting, lineage, and compliant access controls
• Collaborate with analysts and product teams to translate business requirements into robust data solutions and clear data contracts/SLOs
• What Success Looks Like (First 3–6 Months):
• You onboard and productionize new data sources with reliable refresh (scheduled or real‑time)
• You deliver trusted, well‑documented datasets consumed by analytics and operational teams
• Key business entities are clearly modeled and discoverable
• Pipelines have meaningful monitoring and alerting, with reduced failures/re‑runs
• You contribute to standards/templates that speed up future onboarding