About the Role We're looking for a Data Engineer with real production experience to join a US-based fintech company building AI-driven banking applications and its own platform for bank customers. The AI team is small and growing, and this role supports the data side of that buildup. The platform runs on Postgres with pgvector today, with a planned move to Snowflake as the company expands with partners. All code is Python, and the work sits in a regulated financial services setting where traceability, audit, and security matter. Day to day, you'll work with the team lead who runs execution for the banking applications team, alongside the platform and AI developers. Because of the client's security requirements, you'll work in a secure, isolated environment set up for this role: code moves through source control, data is redacted, and there is no direct access to production systems or customer data. What You'll Do Data Pipelines & Storage • Build and maintain ETL pipelines, using Airflow and PySpark where the work calls for it • Work with Postgres and pgvector today, and support the move to Snowflake as the platform grows • Write production Python for data and integration work, turning assigned tasks into working code the team can review and push into its environments Supporting the AI Platform • Provide the data that the company's AI systems depend on, including retrieval-augmented generation (RAG) and semantic analysis • Work closely with the team lead and the AI and software developers on day-to-day execution Quality, Compliance & Delivery • Build for traceability and auditability, as a bank-facing platform requires • Make pipelines and components testable and reusable, and check your work thoroughly before calling it done • Ensure your components are production-ready so releases succeed and outages are avoided What We're Looking For Non-Negotiables • Data engineering experience with strong Python • Proven production experience: you've taken a system from code base to production (home-lab or experimental projects alone won't meet this bar) • Working understanding of the math behind semantic analysis and RAG • Ability to jump into an unfamiliar code base quickly, spot problems, and turn tasks into shipped code • Diligence: you test and check before saying done, and you don't send back avoidable errors • A learning mindset—you keep up with how the industry is moving, including outside working hours • Strong English communication for working closely with a US-based team • Availability during US Central Time business hours, with a device and home setup that meet the client's security requirements Preferred Qualifications • 5+ years of development experience (AI experience can be shorter given how new the field is); senior-level background preferred • Experience with Airflow and PySpark for ETL • Experience with Postgres and pgvector; Snowflake exposure is a plus • Familiarity with LangChain • Experience running models on-premises or integrating into regulated environments • Background in security systems or financial services • Comfort using AI development tools (e.g., Claude) in your daily workflow • Experience working alongside senior developers and being measured on outcomes—features shipped and production readiness • Machine learning depth isn't required today, though the role may grow in that direction over time.

Data Engineer (Python | AI Platform)

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