Overview

Data & Business Intelligence Engineer – Investment Technology

Responsibilities

• This is not a greenfield\ "build everything from scratch\ " engineering role — the corepipeline 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.
• 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

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
• 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
• 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
• 12–18 LPA INR (Annual)