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Indago Capital
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Gurugram · On-site · 5-8 Yrs · Oct 10
3 views
1 open role0 posted in 7d1 in 30d
Hiring: Engineering
Job description
Overview
Indago Capital is a New York-based private credit and structured finance investment firm managing institutional capital across asset based finance strategies.
Responsibilities
- 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.
- 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, CoStar, 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
- 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
- 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 DealCloud with the data warehouse; automate deal ingestion via email parsing and API hooks
- Build an automated pipeline that extracts deal records, contact activity, and pipeline stage data from DealCloud 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.
- 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.
- Build automated pipelines connecting CoStar, 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.
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, BigQuery, 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.
Nice to have
- 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 DealCloud, Salesforce, or Dynamo
- Exposure to LLM APIs (OpenAI, Anthropic Claude, etc.) and building AI-assisted document processing or data extraction workflows
- Experience with CoStar, Bloomberg, Trepp, Intex, or comparable data sources
- Experience with BI/visualization tools: Tableau, Power BI, Looker, or custom dashboard frameworks
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Job application FAQ
Last updated 3 November 2026
- How do I apply for Data Engineer - Asset Based Finance at Indago Capital?
- Open this job on Feedback AI and sign up free (or sign in). You can apply from the job detail page; match scores and skill gaps appear when your profile is complete. Indago Capital may attach structured assessments to interview rounds after you apply.
- Does Feedback AI show a match score for this Data Engineer - Asset Based Finance role?
- Yes after you add a resume or skills. Match score ranks this role against your profile — skills met, gaps, level, and location — so you prioritise applications where fit is strongest.
- Is Data Engineer - Asset Based Finance remote or on-site in Gurugram, Haryana?
- This listing is marked on_site with location Gurugram, Haryana. Confirm details on the job description before applying.
- What skills matter for this Data Engineer - Asset Based Finance role at Indago Capital?
- This is a mid-level Data Engineer - Asset Based Finance role. Key skills include skills listed in the job description. Feedback AI highlights matched vs missing skills on your profile after signup.