Data Engineer

Prodigal is the connected AI platform leading financial institutions use to run their operations.

We work with banks, lenders, credit unions, and other financial companies that lend money to people and manage those relationships over time.

These institutions make millions of high-stakes decisions every day. Who should they reach? When should they reach them? What should they say or offer? When should a case move to a human? How should that change based on the borrower, the account, previous interactions, and the regulations involved?

Getting those decisions right requires a deep understanding of the people, processes, rules, and edge cases behind them.

Prodigal has spent the last eight years building that understanding. More than a billion interactions between financial institutions and their customers have shaped the intelligence, guardrails, and AI agents we now run in production across North America.

Today, our AI agents analyze conversations, capture context, guide human agents, decide the next action, conduct customer conversations, orchestrate outreach, and help people complete payments and resolutions. They are connected, so what is learned in one interaction can inform what happens next.

We are expanding this swarm of AI agents across more of the work financial institutions do: originations, document processing, back-office workflows, servicing, and other critical operations where money, identity, people, and regulation intersect.

We are backed by Y Combinator, Accel, and Menlo Ventures, and work with 100+ financial institutions across North America.

We are looking for a passionate and driven Data Engineer to join our team. You will be instrumental in building scalable data pipelines, generating powerful insights, and supporting our AI/ML initiatives. If you enjoy working across data engineering and analytics and want to help shape the future of Agentic AI, we'd love to hear from you!

• →Design, build, and manage robust data pipelines for collecting, transforming, and modeling data effectively within our Databricks data lake.
• →Turn raw data into clean, reliable, and tested assets using SQL and modern transformation tools like dbt.
• →Collaborate closely with cross-functional teams to deliver actionable insights that drive strategic products, AI, and business decisions.
• →Contribute to AI research initiatives by validating model performance and supporting data needs for machine learning projects.
• →Identify and address performance bottlenecks in data processing, analytics, and reporting.

• 1 year of professional experience in data engineering
• Working with data querying and scripting languages (e.g., SQL, NoSQL, Python/R), 
• Experience building and maintaining data pipeline processes using tools like Airflow, SQL tasks, and stored procedures.
• Working knowledge of data visualization tools like Tableau, Hex, or Power BI.
• Experience working with AWS services such as Lambda, S3, Cloudfront, SQS and more
• Good problem solving, critical thinking, and communication skills. Should be able to find the right balance between perfection and speed of execution
• Self-starter and self-learner, who is comfortable working in a fast paced environment while continuously evaluating emerging technologies
• Bonus: Foundational knowledge in fundamentals of Machine Learning and Artificial Intelligence

Mode of Work - In-Office (Koramangala,Bengaluru)

• Products built using Python, Node.js, React.js, Devin, Cursor
• Databases such as MongoDB, PostgreSQL, Redis, Databricks
• Deployments on EKS, EC2, Lambda and other AWS services