Website:
fomogo.in
Job details:
Note: This role is not for Fomogo, but one of our clients, NextAlphaAI
Seed-funded startup building AI products for securities markets. Founders with 50+ years in software and financial services, with prior exits. Our first product (InvestorAI) is shipping to broker clients now, with a standalone data product and international expansion planned over the next 12 months.
We're looking for a data engineer who wants to build their career at the intersection of data engineering and financial markets — someone for whom working on financial data isn't a stepping stone to something else, but the destination.
The Role
Every AI signal we surface to investors depends on financial data being correct — right values, right units, right company, right period. You'll build and maintain the pipelines that ingest broker research documents and structured financial data, validate them through multi-stage quality gates, and route errors for resolution. The pipeline is specced and partially built. You're here to extend it, harden it, and keep it running reliably as we onboard more brokers and expand coverage.
What You'll Do
- Build and maintain Python data pipelines for ingesting financial documents (PDFs, CSVs) and structured financial data.
- Work with DAG-based orchestration (Airflow or equivalent) to manage pipeline scheduling, retries, and error routing.
- Parse and extract content from financial documents — research notes, filings, earnings transcripts. A parsed field that's wrong is worse than no field at all.
- Write and maintain data validation logic — unit checks, schema validation, plausibility checks against historical values.
- Triage and resolve data quality errors — understand what went wrong, fix it at the source, not just the symptom.
What We're Looking For
- Strong Python — you've built something real with it, not just scripted. Data processing, file parsing, API calls, error handling.
- Experience with or strong understanding of DAG-based pipelines — Airflow, Prefect, or equivalent. Academic projects count if they're substantial.
- Comfort with unstructured data — PDFs, CSVs with inconsistent formats, documents that don't behave. Libraries like pdfplumber or PyMuPDF are a plus.
- SQL for reading and writing structured data stores.
- Genuine interest in financial markets and financial data — you follow markets, you've thought about how companies report earnings, or you've built something finance-related. This isn't a box to tick. Engineers who find financial data genuinely interesting stay and grow; engineers who don't move on within a year.
- You care when data is wrong. A financial value that's off by 10× because of a unit error isn't a minor bug — it produces a wrong signal to an investor. We need engineers who feel that, not engineers who shrug and move on.
- Strong academic background in CS or engineering — NIT, IIIT, or equivalent with demonstrable project work.
Nice to Have
- Internship or project in fintech, financial data, or a data-heavy startup.
- Exposure to financial statements — understanding the difference between consolidated and standalone results, what EPS means, why sign conventions in accounting matter.
- Experience with data quality frameworks or validation libraries.
Why This Role
Financial data is harder than it looks — the errors are subtle, the consequences of getting it wrong are real, and building the system that catches them before they reach investors is genuinely interesting engineering work. If you want to become an expert in financial data infrastructure — the layer that every fintech product is built on — this is the right place to do it. You'll work directly with the founders and data engineering team, own real pipeline components from day one, and build domain expertise that compounds over time as we expand to new markets and data types.
Click on Apply to know more.