Data engineer - asset based finance

Indago Capital is a New York-based private credit and structured finance investment firm managing institutional capital across asset based finance strategies. Our team deploys rigorous, data-driven underwriting across the full deal lifecycle—from sourcing and screening through execution, portfolio monitoring, and investor reporting. As we scale our investment platform, we are building the data and technology infrastructure to match the depth and precision of our analytical process. This Gurugram-based role is a critical part of that build.

01 | ROLE OVERVIEW

This is a high-impact, foundational hire. As a core member of our data and technology function, you will build and maintain the operational data infrastructure that connects our key systems—from investment data and servicer data ingestion through portfolio monitoring dashboards and automated reporting feeds. You will work closely with investment and COO-office teams in New York, translating day-to-day workflows into reliable, scalable data pipelines. This is a greenfield build: the systems you create will define how this firm operates.
This is not a typical engineering role. You need to understand loan level datasets and financial analytics as fluently as you understand database normalization. If you've never worked with loan level data, this isn't the right fit.

You will be the connective tissue between our deal data, our analytical team, and the systems that drive investment decisions.
If you want to understand how private credit actually works—and build the infrastructure that makes it more precise—this is your seat.

02 | KEY RESPONSIBILITIES

Data Infrastructure & Pipeline Engineering

Design and build a centralized, structured data warehouse to consolidate deal data across static attributes, monthly performance updates, and time series position data Develop and maintain automated ETL/ELT pipelines ingesting data from servicer tapes, investment accounting systems, and third-party data sources (Intex, DV01, Co Star, Bloomberg, etc.) Implement a full data management lifecycle across the warehouse: source ingestion, cleaning and normalization, certification, and distribution to downstream consumers Ensure all pipelines are production-grade: idempotent, versioned, monitored, and documented

Portfolio & Operational Data Connections

Automate ingestion and processing of monthly servicer files to feed portfolio dashboards, covenant monitoring tools, and asset surveillance workflows Support position reconciliation workflows and exposure reporting at both the deal and fund/SMA level

AI & Tooling Enablement

Partner with the investment team to deploy AI-assisted workflows: document screening, data extraction, and servicer performance monitoring Stand up Claude and other LLM tooling integrated with firm data sources Integrate Deal Cloud with the data warehouse; automate deal ingestion via email parsing and API hooks

03 | EXAMPLE PROJECTS IN YEAR ONE

Project A: Deal Cloud → Warehouse Pipeline
Build an automated pipeline that extracts deal records, contact activity, and pipeline stage data from Deal Cloud via API, transforms and normalizes the data, and loads it into a cloud data warehouse. Outcome: the investment team can query live pipeline and historical deal data in SQL without manual exports.
Project B: Covenant & Portfolio Surveillance Dashboard
Ingest monthly servicer tape files across 20+ portfolio positions, map covenant triggers (DSCR floors, advance rates, concentration limits), and surface breaches or early-warning signals in a live dashboard alongside position-level P& L and cash flow data. Outcome: the PM team has a single pane of glass for portfolio health instead of 20 separate Excel files.
Project C: Multi-Source Data Integration
Build automated pipelines connecting Co Star, Bloomberg, and servicer data feeds into a unified data warehouse, with scheduled refreshes, data quality checks, and distribution to downstream dashboards. Outcome: the investment and COO-office teams have a single, query able source of truth across external data sources—no manual downloads, no stale spreadsheets.

04 | REQUIRED QUALIFICATIONS

5-10 years of data engineering experience in a professional, production environment Expert-level SQL; ability to write complex queries, optimize performance, and design clean, normalized schemas Proficiency in Python for data transformation, pipeline orchestration, and API integrations Experience with cloud data warehouses: Snowflake, Big Query, Redshift, or Databricks Comfort working with REST APIs to extract data from CRMs, financial data platforms, and third-party providers Basic familiarity with asset-backed finance instruments (ABS, CLOs, CMBS, or similar), mortgage loans or consumer loans—enough to understand data structures and field names, not to model them. Understanding of deal-level and collateral-level data lineage. Strong documentation habits and a bias for maintainable, well-tested code Ability to work effectively in a cross-timezone environment, collaborating closely with teams in New York. Comfortable operating in start-up environments — fast iteration, low bureaucracy, high accountability. Bias towards simplicity, automation and data-driven decision making.

05 | PREFERRED QUALIFICATIONS

Some exposure to financial services data: private credit, structured finance, asset management, or fintech—enough to understand the domain context without needing investment-level expertise Experience integrating with CRM platforms such as Deal Cloud, Salesforce, or Dynamo Exposure to LLM APIs (Open AI, Anthropic Claude, etc.) and building AI-assisted document processing or data extraction workflows Experience with Co Star, Bloomberg, Trepp, Intex, or comparable data sources Experience with BI/visualization tools: Tableau, Power BI, Looker, or custom dashboard frameworks