Financial Data Engineer (m/w/d)

About The Index Bakery

The Index Bakery is a fintech start-up using AI to automate and fundamentally reinvent how financial indices are built and managed. What used to require slow, Excel-heavy workflows and months of development time, The Index Bakery compresses into hours — through a technology-driven platform that handles the full indexing lifecycle, from design and backtesting to daily calculation and distribution.

The platform is cross-asset by design, covering equities, fixed income, commodities, and crypto. The Index Bakery calculates institutional-grade reference rates across digital assets — transparent in methodology, precise in execution, and built for professional use.

We scale through code, not headcount. And that’s exactly the kind of thinking we’re looking for in this role.

What you’ll do

— Ingest and unify market data from multiple vendors into a clean, consistent data model — the foundation everything else is built on.

— Build reliable pipelines and validation frameworks that ensure data quality and catch inconsistencies before they cause problems downstream.

— Verify the correctness of market data and resolve discrepancies across sources — you’ll be the person who knows when a number looks wrong and why.

— Prototype new data products rapidly, then make the call on when something is ready to move from PoC to production.

— Collaborate with product and analytics teams to make data accessible, trustworthy, and decision-ready.

— Stay curious: explore new tools and technologies to continuously improve how we process and manage financial data.

What you bring

— Strong foundation in data engineering — you understand how data flows, how models are structured, and what “production-ready” actually means.

— Advanced Python skills, including experience with data processing frameworks and working with APIs.

— Hands-on experience with market data: vendors, data structures, common quality issues, and how to validate against them.

— Familiarity with modern data technologies such as Spark, Kafka, SQL, or cloud-based data stacks.

— A sharp eye for correctness — you don’t just move data, you question it.

— Willingness to build genuine domain knowledge in financial markets over time.