Job Opportunities API

About Rimes Rimes provides the Intelligence Fabric for Capital Markets, a trusted data network and intelligence architecture that transforms fragmented data, operations and workflows into decision-grade intelligence. The world’s leading institutional investors, asset managers, and service providers rely on Rimes to help them make better investment decisions that power more than US$ 75 trillion in AUM annually. 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 & 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.    Requirements: 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.  Nice To Have: Palantir Foundry: pipelines/transforms, Code Repos, Ontology, and operational applications.  Spark execution concepts: partitions, shuffles, caching, and performance optimization.  Exposure to Databricks or cloud‑native compute with compute pushdown.  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.  What We Offer: Private Medical Insurance  Private Provident Fund  26 days of annual leave  5 days paid sick leave  Breakfast and snacks  Smoothie Fridays  Compensation: Competitive pay and bonus eligibility  Work Life Balance: Flexible hybrid work environment  Only selected candidates will be contacted for interviews. We appreciate your understanding. Thank you for considering a career with us. Rimes