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. Listen directly from our CTO, Cofounder - Sangram Raje
About the role -
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!
🏆 Responsibilities- Design, build and manage robust data pipelines for collecting, transforming and modeling data within our Databricks data lake - dbt is your primary tool, not an afterthought
- Own the dbt layer end to end - models, sources, tests, macros, incremental strategies and documentation. If it touches transformation, it goes through dbt and you're accountable for it being clean, tested and re-runnable
- Turn raw, messy data into reliable, well-modeled assets that downstream teams can actually trust - no untested models, no undocumented logic, no shortcuts
- Collaborate closely with cross-functional teams to deliver actionable insights that drive product, AI and business decisions - translating business logic into dbt models that are built to last
- Support AI and ML initiatives by ensuring clean, validated and well-structured data is always available when the models need it
- Identify and fix performance bottlenecks in dbt models, Databricks queries and downstream reporting pipelines
- 2-3 years of hands-on experience in data engineering - you've built things in production, not just in notebooks
- Strong SQL - you can write and reason about complex joins, aggregations and window functions, and you know why a query is slow before you tune it
- Python for data engineering with production-grade PySpark or Spark SQL experience. Databricks strongly preferred
- Dimensional modeling - star schemas, fact vs dimension grain, SCD Type 2, and incremental loads you can confidently re-run without breaking things
- dbt as your transformation framework of choice - models, tests, sources and incremental strategies are second nature to you
- AWS hands-on experience across Lambda, S3, CloudFront, SQS and beyond
- AI-native workflow - we ship with Claude Code and Cursor and we expect you to use them well, not just know they exist
- Sharp problem solving - you find the right balance between getting it right and getting it done, and communicate clearly when tradeoffs are being made
- Self-driven and curious - you thrive in fast paced environments, pick things up quickly and keep an eye on what's emerging in the data ecosystem
- Bonus - Foundational knowledge in ML/AI fundamentals
Mode of Work - In-Office (Koramangala,Bengaluru)
⚙️ Our Tech Stack- Products built using Python, Cursor, Claude, dbt, Airflow
- Databases such as MongoDB, PostgreSQL, Redis, Databricks,
- Deployments on EKS, EC2, Lambda and other AWS services
Health insurance for you and your family, meals at office on us, travel reimbursement, unlimited leaves, subsidized gym membership, unlimited learning & development, flexible work schedule and a world-class team to learn and grow with!
From day 1, Prodigal has been defined by talented, humble, and hungry leaders and we want this mindset and culture to continue to blossom from top to bottom in the company. If you have an entrepreneurial spirit and want to work in a fast-paced, intellectually-stimulating environment where you will be pushed to grow, then please reach out because we are looking to build a transformational company that reinvents one of the biggest industries in the US.
To learn more about us - please visit the following:
Our Story - https://www.prodigaltech.com/our-story
What shapes our thinking - https://link.prodigaltech.com/our-thesis
Our website - https://www.prodigaltech.com/
Skills Required
- At least 1 year of professional data engineering experience
- Experience with data querying and scripting languages such as SQL, NoSQL, Python, or R
- Experience building and maintaining data pipelines using Airflow, SQL tasks, and stored procedures
- Working knowledge of Tableau, Hex, or Power BI
- Experience with AWS services such as Lambda, S3, CloudFront, and SQS
- Problem-solving, critical-thinking, and communication skills
- Ability to work independently and learn continuously in a fast-paced environment
- Foundational knowledge of machine learning and artificial intelligence
What We Do
Prodigal is a pioneer of Collection & Servicing Intelligence, a new category of AI software, which enables banks, lenders and collection agencies of all sizes to quickly and efficiently collect accounts receivables. Our cloud-native, Collection & Servicing Intelligence platform delivers actionable insights for banks, lenders, and ARM agencies to maximize revenue, optimize operations, and minimize compliance risk. Prodigal delivers artificial intelligence and machine learning capabilities to lenders and ARM agencies. Prodigal restores value from accounts past due and improves servicing productivity while retaining customer loyalty. We empower entire teams from executive leadership to representatives with data and insights needed to segment and prioritize accounts, to enhance portfolio yield, and to address procedural and legal (TCPA, FDCPA, UDAAP, ...) non-compliance. With our Collection & Servicing Intelligence Platform, senior executives have complete intelligence about expected liquidation, aggregated agent productivity, and FDCPA/TCPA non-compliance in real-time — an imperative for a modern collections business in an increasingly regulated environment. Prodigal is headquartered in the heart of Silicon Valley (Sunnyvale, CA) and is founded by industry veterans with deep expertise in financial services, predictive modeling, speech AI and core engineering. Our investors include top tier investors like Menlo Ventures, Accel andY Combinator. Prodigal has been featured in TechCrunch, American Banker, CBS News, Fortune, insideARM and other leading technology and financial services news sites.
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