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# Data Engineer

- **Company:** Alter Domus Luxembourg S.à r.l.
- **Location:** Chicago, IL, United States
- **Experience:** Expert
- **Salary:** $87,500.0 - $145,000.0
- **Contract:** Permanent contract
- **Skills:** HTML, Airflow, Amazon Web Services, Microsoft Azure, BigQuery, Continuous Integration, Data Architecture, Information Engineering, Data Infrastructure, Data Integrity, Extract Transform Load (ETL), Data Mining, Data Structures, Xbrl, Python (Programming Language), Parsing, Standard Sql, Data Streaming, Workflow Management Systems, Cloud Platform System, Data Ingestion, Delivery Pipeline, Snowflake, Change Data Capture, Event Driven Architecture, Star Schema, Real Time Data, Data Management, Data Pipelines, Amazon Redshift, Databricks
- **Published:** October 20, 2026
- **Apply:** https://jobs.alterdomus.com/talentcommunity/apply/1405167333/?locale=en_GB

## About the Role

* 5+ years of professional data engineering experience building production pipelines at scale
 * Expert proficiency in Python and SQL
 * Hands-on experience with modern cloud data platforms (e.g., Snowflake, Databricks, BigQuery, Redshift) and transformation frameworks (e.g., dbt)
 * Experience with orchestration tools (e.g., Airflow, Dagster, Prefect) and CI/CD for data pipelines
 * Strong experience with CDC, streaming, or event-driven architectures
 * Solid understanding of data modelling - star schema, slowly changing dimensions, master data management
 * Experience in financial services data environments preferred

Desirable:

 * Experience with regulatory filing ingestion and parsing (e.g., XBRL, HTML extraction from public filings)
 * Familiarity with loan-level data structures (credit agreements, facilities, tranches, pro-rata allocations)
 * Experience building pipelines that serve external commercial clients (not only internal BI)
 * Cloud platform certifications (AWS, Azure, or GCP)

## Description

You will build and maintain the data pipelines that power a new suite of private credit analytics products. This means extracting data from large-scale loan administration systems, transforming it into analytics-ready structures, implementing incremental refresh logic for near-real-time delivery, and resolving entities across multiple sources. You own the "data in motion" layer - everything from source extraction through to landing clean, validated, reconciled data into the platform for downstream consumption., * Pipeline Build & ETL: Design, build, and maintain production-grade ETL/ELT pipelines to extract, transform, and load data from core administration platforms into the analytics data model. Handle tens of thousands of live records with daily (or intraday) refresh requirements

 * Data Extraction & Transformation: Implement extraction logic for complex financial data - including loan balances (funded, unfunded, PIK, outstanding), rate structures (index, floor, spread, all-in), pro-rata allocations, utilisation calculations, and maturity dates
 * Incremental Refresh Logic: Implement change-data-capture (CDC) and incremental refresh patterns to deliver near-real-time data without full reloads. Critical for client-facing products where same-day freshness is expected
 * Supplementary Data Ingestion: Build pipelines to ingest and validate structured data from regulatory filings and third-party sources, de-duplicate against the primary dataset, and integrate into the master data layer
 * Data Quality & Monitoring: Implement automated quality checks - completeness, freshness, range validation, referential integrity, duplicate detection. Build alerting for pipeline failures and anomalies
 * Collaboration: Work closely with the Data Architect (who defines the logical model and schema) and the Platform Engineer (who owns the infrastructure your pipelines run on) to ensure alignment between logical design, physical implementation, and delivery requirements