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 setting 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
12–18 LPA INR (Annual)

📌 Data & Business Intelligence Engineer (Shivamogga)
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📍 Shivamogga