Data Reliability Engineer

Selby Jennings; Chicago (IL); On-site; USD 110,000 - 160,000; Full time

Job summary

Selby Jennings is seeking a Data Reliability Engineer to join a high-performance data engineering team supporting trading and research. The role blends data engineering, platform reliability, and operational support to ensure datasets, pipelines, and APIs remain accurate and production-ready.

You will act as a primary data contact, monitor pipelines, onboard new datasets, and develop scalable ETL/ELT processes across real-time and analytical workloads.

Qualifications

• 3+ years of experience in Data Engineering, Data Reliability, Site Reliability or Data Operations.
• Strong Python development experience with Pandas, PyArrow, and Spark.
• Experience building and supporting large-scale data pipelines and APIs.
• Solid data modeling, data quality, and data architecture knowledge.
• Experience with cloud data platforms and lakehouse technologies such as Databricks, Delta Lake, or AWS.
• Exposure to financial market data or capital markets environments is a plus.
• Proven ability to troubleshoot production issues and drive operational improvements.
• Strong communication skills in fast-paced, business-critical environments.

Responsibilities

• Act as primary point of contact for data-related issues across trading, research, and business teams.
• Investigate and resolve data quality, availability, and freshness issues.
• Monitor data pipelines, workflows, and real-time feeds; respond to incidents.
• Coordinate with external data providers to resolve feed issues and discrepancies.
• Communicate incident status and resolutions to stakeholders.
• Integrate and onboard new datasets with schema mapping, transformation, validation, and history backfills.
• Develop scalable ETL/ELT pipelines for real-time and analytical workloads.
• Build data quality frameworks, monitoring, and automated validation processes.
• Design and maintain normalized data models for complex financial datasets.
• Develop and support APIs delivering data to trading, research, and analytics platforms.
• Partner with engineering, quantitative, and business teams to deliver reliable data solutions.
• Enhance observability, monitoring, and tooling to improve reliability and performance.

Skills

Data Engineering

Python

Pandas

PyArrow

Spark

Tools

Databricks

Delta Lake

AWS

Job description

We're seeking a Data Reliability Engineer to join a high-performance data engineering team supporting trading and research functions. This role blends data engineering, platform reliability, and operational support, with responsibility for ensuring critical datasets, pipelines, and APIs remain accurate, scalable, and production-ready.

Data Operations & Support

• Act as a primary point of contact for data-related issues across trading, research, and business teams.
• Investigate and resolve data quality, availability, and freshness issues.
• Monitor data pipelines, workflows, and real-time feeds, responding to alerts and incidents.
• Coordinate with external data providers to resolve feed issues, data discrepancies, and specification changes.
• Communicate incident status and resolutions to stakeholders.

Data Engineering & Reliability

• Integrate and onboard new datasets, including schema mapping, transformation, validation, and historical backfills.
• Develop and maintain scalable ETL/ELT pipelines supporting real-time and analytical workloads.
• Build data quality frameworks, monitoring solutions, and automated validation processes.
• Design and maintain normalized data models for complex financial and market datasets.
• Develop and support APIs that deliver data to trading, research, and analytics platforms.
• Partner with engineering, quantitative, and business teams to deliver reliable data solutions.
• Enhance platform observability, monitoring, and operational tooling to improve reliability and performance.

Qualifications

• 3+ years of experience in Data Engineering, Data Reliability, Site Reliability Engineering, Software Engineering, or Data Operations.
• Strong Python development experience, including data processing frameworks such as Pandas, PyArrow, and Spark.
• Experience building and supporting large-scale data pipelines and APIs.
• Strong understanding of data modeling, data quality, and data architecture principles.
• Experience with cloud data platforms and lakehouse technologies such as Databricks, Delta Lake, or AWS.
• Exposure to financial market data, trading systems, or capital markets environments is a plus.
• Proven ability to troubleshoot production issues, manage incidents, and drive operational improvements.
• Strong communication skills and experience working in fast-paced, business-critical environments.