Rimes Technologies
Financial Services
DataEngineer
Neural analysis suggests this role is
optimal for Entry candidates.
“Data Engineer at Rimes Technologies. Skills: Data pipelines, Data onboarding, Python, PySpark. Ingest schedule and automate refreshes. Model data”
What You'll Achieve.
Onboard and productionize new data sources; Deliver trusted, well-documented datasets; Model key business entities; Pipelines have monitoring and alerting; Reduced failures/re-runs; Contribute to standards/templates
Industry & Context.
Solve complex data problems
What They're Looking For.
Must Have
1-3 years in data engineering, End-to-end pipeline delivery, Proficiency in Python, Proficiency in PySpark, SQL for analytical logic, SQL for transformation logic, Data modeling skills, Experience with data ingestion, Solid grasp of data quality, Solid grasp of testing, Solid grasp of observability, Solid grasp of lineage, Solid grasp of governance, Comfort working with large datasets, Comfort working with distributed compute, Comfort working with modern ELT patterns
Nice to Have
Palantir Foundry experience, Spark execution concepts, Experience with Databricks, Experience with cloud-native compute, Experience with financial data, Experience with enterprise operational data, Experience with AI-assisted ETL/ELT, Experience with data quality tooling, Familiarity with streaming frameworks, Familiarity with orchestration tools
What You'll Do.
Ingest schedule and automate refreshes
Support analytics applications
Support operational applications
Implement data quality checks
Implement observability
Implement compliant access controls
Collaborate with analysts
Collaborate with product teams
Translate business requirements
Build robust data solutions
Create clear data contracts
Onboard new data sources
Productionize new data sources
Deliver trusted datasets
Deliver documented datasets
Model key business entities
Make entities discoverable
Contribute to standards
Contribute to templates
How You'll Work.
Team & Collaboration
Data producers; Analysts; Product teams; Domain experts
Full Job Description
About Rimes 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. The Opportunity: 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, primarily within Palantir Foundry (our core data platform) and complementary cloud compute. Note: Experience with Palantir Foundry 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 Foundry. Responsibilities: Ingest 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 sta
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