Data & Business Intelligence Engineer – Investment Technology
India (Remote) | Full-Time | Permanent | Venture Capital / Investment Tech
Placed by Hotsourced – connecting top Indian talent with world-class global companies
Our client is a venture capital firm that has built a proprietary data infrastructure — a custom Airtable-based CRM feeding into a BigQuery data warehouse — to track deal flow, portfolio performance, and investment decisions. The core engineering foundation is already in place; this role is about operating, extending, and unlocking insight from that system , working closely with a non-technical investment team.
This is a high-autonomy, high-trust role. You'll be the primary point of contact translating ambiguous business questions from investment professionals into accurate, reconciled answers — while also identifying opportunities to enrich and improve the underlying data using AI and automation.
Company Overview
Our client is a venture capital firm modernizing how deals are sourced, tracked, and evaluated. Their data stack — Airtable as the CRM/source of truth, BigQuery as the data warehouse, with lightweight automation layered on top — supports an investment team that needs fast, accurate answers to questions about their deal pipeline and portfolio.
Role Overview
As Data & BI Engineer, you'll own the day-to-day health and usability of an existing Airtable → BigQuery data pipeline. The majority of your work will involve writing and reasoning about SQL queries against BigQuery, reconciling data discrepancies, and translating ambiguous requests from non-technical stakeholders into precise, correct answers. A smaller but growing part of the role involves using AI/automation to enrich data where high-quality external datasets (e.g., Crunchbase, PitchBook) aren't practically accessible.
This is not a greenfield "build everything from scratch" engineering role — the core pipeline architecture already exists. Success looks like: stakeholders get accurate answers quickly, data discrepancies get caught and resolved before they reach leadership, and the underlying data quality improves over time.
Key Responsibilities
- Write and maintain SQL queries against BigQuery to answer recurring and ad hoc business questions from the investment team (e.g., deal flow funnel analysis, conversion rates, portfolio metrics)
- Proactively identify and resolve data reconciliation issues arising from the Airtable → BigQuery data flow (e.g., duplicate company/deal records, inconsistent field definitions, snapshot vs. event-level data mismatches)
- Translate ambiguous, non-technical requests into precise technical definitions — clarifying with stakeholders rather than assuming intent (e.g., "deals seen" could mean multiple different things depending on context)
- Support and extend the existing Airtable CRM, including feature requests, automations, and data cleaning as the schema evolves
- Maintain and lightly extend existing Airflow (Cloud Composer)-orchestrated data pipelines and Cloud Functions as needed
- Explore opportunities to use AI (e.g., Google AI Studio/Gemini, LLM-based enrichment) to fill data gaps where standard market-data subscriptions (Crunchbase, PitchBook, Harmonic) aren't a practical option
- Maintain data quality and governance standards across the pipeline
- Communicate findings and data limitations clearly to non-technical, finance-oriented stakeholders
Required Qualifications
- Strong, hands-on SQL skills — comfortable writing complex queries independently against a cloud data warehouse (BigQuery preferred)
- Demonstrated experience reconciling data discrepancies and diagnosing "why don't these numbers match" problems in a real production dataset
- Working knowledge of BigQuery and the broader GCP ecosystem
- Experience with Airtable, including automations and/or scripting (or comparable low-code CRM/database tooling)
- Excellent written and verbal communication skills, with proven ability to translate technical findings for non-technical business stakeholders
- Strong independent judgment — comfortable working with ambiguity and making a judgment call on how to proceed without heavy oversight
- Experience working in a remote, asynchronous, high-autonomy setup
Preferred / Nice to Have
- Experience with Apache Airflow (Cloud Composer) for pipeline orchestration
- Familiarity with Google Cloud Functions and Python for lightweight automation
- Exposure to AI/LLM-based workflows (RAG, prompt engineering, agentic automation) — particularly for data enrichment use cases
- Understanding of the venture capital lifecycle (sourcing, due diligence, investment committee, portfolio management)
- Familiarity with financial concepts such as IRR, MOIC, and cap tables
- Experience with data enrichment or web-scraping tools (e.g., PeopleDataLabs, Clearbit)
- Prior experience in a startup or lean team environment where you were the sole owner of a data function
What Success Looks Like (3–6 Months)
- The investment team can get accurate answers to standard data questions instantly and without errors
- Recurring data quality issues are identified and resolved proactively rather than reactively
- You're beginning to surface additional insights and analysis on your own initiative, not just responding to requests
- Progress toward enriching incomplete data using AI/automation where paid data sources aren't accessible
Work Schedule
Monday to Friday 08:00 AM – 05:00 PM GMT / 01:30 PM – 10:30 PM IST
Compensation