Data Engineer

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 for the world's leading institutional investors, asset managers, and service providers.

• Ingest and 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 and stream refreshes;
• Model and operationalize data 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 and SLOs;
• Onboard and productionize new data sources with reliable scheduled or real-time refresh;
• Deliver trusted and well-documented datasets consumed by analytics and operational teams;
• Ensure key business entities are clearly modeled and discoverable;
• Implement meaningful monitoring and alerting for pipelines with reduced failures and re-runs;
• Contribute to standards and 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 and 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.

• Private Medical Insurance;
• Private Provident Fund;
• 26 Days of annual leave;
• 5 Days paid sick leave;
• Breakfast and snacks;
• Smoothie Fridays;
• Competitive pay and bonus eligibility;
• Flexible hybrid work environment;
• Relocation support is available.