Data Operations Engineer

Thurn Partners; Chicago (IL); On-site; USD 250,000 - 500,000; Full time

Job summary

Thurn Partners Ltd. is building a Global Data Platform team in Chicago focused on making data reliable for traders and researchers.

You will own data reliability, automate workflows with AI, and improve ingestion pipelines and data quality across critical datasets. You will design AI-driven processes, manage ETL/ELT pipelines and collaborate with vendors and internal teams to ensure timely, accurate market data delivery.

Qualifications

• 3 to 10 years as a software engineer, data engineer or SRE in finance.
• Hands-on experience with data ingestion, data quality, monitoring and observability tooling.
• Strong Python, including Pandas, PyArrow and Spark; Go is a plus.
• Experience with Databricks, Delta Lake or Snowflake, plus ETL pipelines and SFTP-based vendor feeds.
• Exposure to real-time or historical market data is an advantage.

Responsibilities

• Own data quality, freshness and availability across business-critical datasets and act as first point of contact for traders, researchers and engineers when something breaks.
• Monitor ingestion pipelines, scheduled workflows and real-time market data feeds and drive incidents through to resolution with clear updates.
• Onboard new datasets: schema mapping, normalization, historical backfills and metadata.
• Build automated data quality checks, monitoring, anomaly detection and observability tooling.
• Design and build AI-driven workflows that remove recurring operational tasks.
• Build and maintain ETL/ELT pipelines and APIs serving market and reference data to trading and research systems.
• Work directly with external data vendors on upstream issues and specification changes.
• Clear routes onward: leading a local or global team, moving into the wider data platform group, or into production and trading operations.

Skills

Python

Pandas

PyArrow

Spark

Go

Databricks

Delta Lake

Snowflake

ETL pipelines

SFTP feeds

Data quality

Observability

Tools

Databricks

Delta Lake

Snowflake

ETL Pipelines

SFTP Feeds

Job description

A leading global market maker trading across the world’s fastest financial markets. Every trading decision runs on data. Historically that data sat in silos by office and desk; the firm is now unifying it on a single global Databricks platform, and the data platform team is expected to reach around 150 people by year-end.

The role

This is a greenfield hire and one of the first of its kind at the firm. You will sit in the Global Data Engineering team in Chicago and own the reliability of the data that traders and researchers depend on every day. Most data teams accept a share of manual operations work as permanent. Here the brief is to automate it away, using AI-driven workflows where they fit.

• Own data quality, freshness and availability across business-critical datasets, and act as first point of contact for traders, researchers and engineers when something breaks
• Monitor ingestion pipelines, scheduled workflows and real-time market data feeds, and drive incidents through to resolution with clear updates
• Onboard new datasets: schema mapping, normalization, historical backfills and metadata
• Build automated data quality checks, monitoring, anomaly detection and observability tooling
• Design and build AI-driven workflows that remove recurring operational tasks
• Build and maintain ETL/ELT pipelines and APIs serving market and reference data to trading and research systems
• Work directly with external data vendors on upstream issues and specification changes
• Clear routes onward: leading a local or global team, moving into the wider data platform group, or into production and trading operations

Qualifications

• 3 to 10 years as a software engineer, data engineer or SRE in finance; trading firms, exchanges, market data providers and investment banks all qualify
• Hands-on experience with data ingestion, data quality, monitoring and observability tooling
• Strong Python, including Pandas, PyArrow and Spark; Go is a plus
• Experience with Databricks, Delta Lake or Snowflake, plus ETL pipelines and SFTP-based vendor feeds
• Exposure to real-time or historical market data (equities, ETFs, fixed income or credit) is an advantage
• A genuine interest in automating operational work, not just running it
• Clear communication with traders, researchers and vendors under pressure
• Legal authorisation to work in the US; sponsorship is not available

Compensation

• Total compensation package of $250,000 - $500,000
• Dependent on experience, level, and interview performance

Pre-Application:

• Please do not apply if you are looking for a contract or remote work
• You must be eligible to live and work in the US, without requiring sponsorship
• Please ensure you meet the required experience section prior to applying
• Allow 1-5 working days for a response to any job enquiry