Data Engineer

Rimes provides enterprise data management solutions to the global investment community. Driven by our passion for solving the most complex data problems, we provide our clients with investment intelligence that powers more than US$75 trillion in assets under management annually. The world’s leading institutional investors, asset managers and service providers rely on Rimes to help them make better investment decisions using accurate information and industry-leading technology.

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. 

• 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. 

• 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. 

• Databricks or cloud‑native compute with compute pushdown. 
• Palantir Foundry: pipelines/transforms, Code Repos, Ontology, and operational applications. 
• Spark execution concepts: partitions, shuffles, caching, and performance optimization. 
• Experience with financial or enterprise operational data. 
• Experience with AI‑assisted ETL/ELT or data quality tooling. 
• Familiarity with streaming frameworks and/or orchestration tools.