Data Engineer
Prodigal
Salary undisclosed
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Description
About Prodigal
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 -Ā
We are looking for a passionate and drivenĀ 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!
š 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
ā RequirementsĀ
- 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
Ā
Ā
Mode of Work - In-Office (Koramangala,Bengaluru)
āļø Our Tech Stack
- Products built using Python, Cursor, Claude, dbt, Airflow
- Databases such as MongoDB, PostgreSQL, Redis, Databricks,Ā
- Deployments on EKS, EC2, Lambda and other AWS services
š What we offer
Top Tier BenefitsĀ
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!Ā
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
Benefits
- 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/
About this role
Prodigal's Data Engineer role centers on owning the transformation layer for a financial AI platform that processes over a billion customer interactions annually. You'll design and maintain dbt pipelines within Databricks that turn raw data into trusted assets for product teams, AI models, and business analytics. This means taking full accountability for model quality, testing, documentation, and performanceāensuring downstream teams can rely on your work without shortcuts or surprises.
The position demands 2ā3 years of production data engineering experience with strong SQL and PySpark fundamentals, plus deep dbt expertise across models, incremental strategies, and testing. You'll collaborate across teams to translate business logic into dimensional models, support ML initiatives with clean validated data, and troubleshoot performance bottlenecks in queries and pipelines. The tech stack includes Databricks, AWS services (Lambda, S3, SQS), Airflow, and AI-native tools like Claude and Cursor.
This is an in-office role in Bengaluru suited for someone who thrives in fast-paced environments, balances pragmatism with rigor, and stays curious about emerging data practices. If you're self-driven, comfortable with dimensional modeling and incremental loads, and want to shape data infrastructure for an AI-first fintech platform backed by Y Combinator and Accel, this could be a strong fit.
Typical pay for this role
$120,000
Typical range: $100,000 to $140,000
This role's local market on JobMinglr
Pay for this role
The employer didn't post a pay range for this role. That usually means pay is set in negotiation, which favors whoever arrives with numbers. Check ranges on comparable Data Engineer postings in Bengaluru, and analyze any offer before you accept it.
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