The client is a global investment management firm that specializes in systematic investment strategies.

Responsibilities:

- The ingestion, quality, and delivery of investment data that feeds our systematic credit research and trading, from raw vendor feeds through to a curated Gold layer

- Onboard new market and reference data: fixed income indices (Bloomberg), fundamental data for public and private issuers (Bloomberg, CIQ, and new private-credit vendors), and orderbook data from multiple street sources

- Migrate legacy batch pipelines to modern orchestration: move fundamental data from monthly snapshots to daily deltas, and rebuild ingestion in Dagster with clear raw/silver/gold separation

- Consolidate overlapping data sources into single, trusted representations (e.g. one Gold orderbook dataset from several vendor feeds) so downstream consumers stop reconciling by hand

- Build and maintain the overrides and correction mechanisms that let researchers and PMs adjust point-in-time data safely

- Put explicit data quality checks in place: detect volume oscillations, missing deltas, and vendor breaks before they reach production models

- Model credit and issuer data thoughtfully: entity resolution across equity and credit universes, point-in-time correctness, and parent/child issuer relationships

- Work directly with the credit research and portfolio teams to turn data needs into reliable, well-documented pipelines

Requirments:

- Strong Python and data engineering skills: building ingestion and transformation pipelines that are reliable, tested, and observable

- Experience with a modern orchestration framework (Dagster preferred; Airflow or similar acceptable) and columnar data tooling (Polars, PyArrow, Parquet)

- Experience using AI-assisted software development tools (e.g., GitHub Copilot, ChatGPT, Claude Code) to improve engineering productivity. Able to critically evaluate, test, and validate AI-generated code to ensure quality, security, and compliance with engineering standards.

- A real understanding of point-in-time data and why look-ahead bias matters in a research and trading context

- Experience onboarding and normalizing third-party market data (index, fundamental, or orderbook data) and reconciling overlapping vendor sources

- Comfort designing data quality and validation layers, not just moving bytes from A to B

- Background in systematic credit, fixed income, or another quantitative investment domain strongly preferred

- GitLab-based CI/CD and containerized deployment experience (Docker, Kubernetes)

- Self-directed and able to own a data domain end to end, from vendor feed to consumer-ready dataset

- Good communication skills; this is a collaborative, small-team environment

Additional information

- Work with some of the most dynamic US tech companies, building and iterating on new features and platforms.

- Long-term projects with real technical challenges.

- Fully remote work with flexible hours.

- Collaboration flexibility: We work with B2B (PFA/SRL) contracts.

- 30 paid days off per year.

- We provide equipment as needed (laptop, desktop, etc.).

- Continuous learning: We sponsor career-improving courses, seminars, and certifications.

- Opportunity for annual business visits to the US, depending on project needs.

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