Data Engineer

ABOUT US:

As a world leading provider of integrated solutions for the alternative investment industry, Alter Domus (meaning “The Other House” in Latin) is proud to be home to 90% of the top 30 asset managers in the private markets, and more than 6,000 professionals across 24 jurisdictions.

With a deep understanding of what it takes to succeed in alternatives, we believe in being different in what we do, how we work, and most importantly in how we enable and develop our people. Invest yourself in the alternative, and join an organization where you progress on merit, where you can speak openly with whoever you are speaking to, and where you will be supported along whichever path you choose to take.

Find out more about life at Alter Domus at

JOB 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.

Key Responsibilities:

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

Requirements:5+ years of professional data engineering experience building production pipelines at scale

Expert proficiency in Python and SQLHands-on experience with modern cloud data platforms (e.g., Snowflake, Databricks, Big Query, 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)

WHAT WE OFFER:

We are committed…