Data & Business Intelligence Engineer

hotsourced; India; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

hotsourced is seeking a Data & BI Engineer for an investment tech client in India (remote). You will own the Airtable→BigQuery data pipeline and translate business questions into SQL queries.

You will also apply AI/automation to enrich data, maintain Airflow pipelines, and collaborate with non-technical investment professionals in a high-autonomy, remote setup.

Qualifications

• Strong SQL skills and experience with BigQuery in production datasets.
• Experience reconciling data discrepancies and diagnosing data issues.
• Familiarity with Airtable automations or scripting.
• Excellent communication to translate technical findings for non-technical stakeholders.
• Experience working in remote/asynchronous, high-autonomy environments.

Responsibilities

• Write and maintain SQL queries against BigQuery to answer recurring and ad hoc business questions.
• Identify and resolve data reconciliation issues in Airtable→BigQuery data flow.
• Clarify ambiguous requests with stakeholders and define technical terms.
• Support and extend Airtable CRM with automations and data cleanup.
• Maintain and extend Airflow pipelines (Cloud Composer) and Cloud Functions as needed.
• Explore AI/automation to enrich data when external datasets are limited.
• Uphold data quality and governance across the pipeline.
• Communicate findings and data limitations clearly to finance stakeholders.

Skills

SQL

BigQuery

Data reconciliation

Airflow

Python

Communication skills

Remote work

Tools

Airtable

Cloud Composer

Cloud Functions

AI/automation

Job description

Data & Business Intelligence Engineer – Investment Technology

India (Remote) | Full-Time | Permanent | Venture Capital / Investment Tech

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