Data Engineering Manager

IL LLP•Chicago, IL

8h•Hybrid

About The Position

The Data Engineering Manager will lead a scrum team of data engineers in the design, development, and delivery of Sidley's enterprise Databricks data platform. This role blends hands-on technical leadership with people management, balancing day-to-day engineering execution with longer-term architectural direction. Partnering closely with the Data Architect, analytics, and business teams, the Data Engineering Manager will set technical standards, drive data quality, and ensure the team delivers scalable, reliable, and governed data solutions.

Requirements

Bachelor's degree in Computer Science, Engineering, Data Science, or a related field.
A minimum of 5 years of hands-on experience in data engineering, including designing and building scalable data pipelines and ETL/ELT processes.
A minimum of 2 years of experience managing or leading a team of data engineers, including direct people management responsibilities.
Strong expertise in Azure Databricks, including Databricks Lakehouse, Delta Lake, Databricks SQL, Apache Spark, Unity Catalog, Databricks Workflows, and Databricks Notebooks.
Proficiency with Python, PySpark, Spark SQL, and SQL for large-scale data processing.
Proven experience with Lakehouse architecture patterns (Bronze/Silver/Gold), schema evolution, and data modeling for analytics and operational workloads.
Demonstrated experience driving code reviews, setting engineering standards, and instilling data quality and testing disciplines within a team.
Experience with CI/CD pipelines, version control, automated testing, and monitoring in a data engineering context.
Hands-on experience with cloud data platforms in Azure, AWS, or GCP, with Azure strongly preferred.
Strong communication and stakeholder management skills, with the ability to translate between technical and business contexts.
Strong organizational and project management skills.
Strong attention to detail and commitment to quality.
Good judgment and sound decision-making under pressure.
Strong interpersonal and communication skills.
Able to work harmoniously and effectively with others across technical and business teams.
Able to preserve confidentiality and exercise discretion.
Able to manage multiple priorities and competing deadlines.
Applicants must be authorized to work in the United States without the need for employer sponsorship, now or in the future.

Nice To Haves

Master's degree in Computer Science, Engineering, or a related field.
Experience integrating Azure Databricks with Azure DevOps, ADLS Gen2, and Azure Key Vault.
Familiarity with enterprise data modeling, data governance frameworks, and metadata management tools such as Unity Catalog or Collibra.
Experience with Infrastructure as Code (IaC) and Governance as Code practices.
Familiarity with machine learning workloads and feature engineering in a Lakehouse environment.
Experience leading data engineering teams in an agile or scrum delivery model.
Industry experience in legal or professional services a plus.

Responsibilities

Manage, mentor, and develop a scrum team of 5-7 data engineers, fostering a culture of technical excellence, collaboration, and continuous improvement.
Conduct regular one-on-ones, performance reviews, and career development conversations to support individual growth and team retention.
Resolve team impediments and shield engineers from organizational friction so they can focus on delivery.
Set and enforce technical direction for the team, including coding standards, design patterns, and engineering best practices across the Databricks data platform.
Lead and participate in technical design sessions, translating complex business and data requirements into scalable, well-architected solutions.
Drive the design and evolution of the Lakehouse architecture (Bronze/Silver/Gold) on Azure Databricks, including Delta Lake, Apache Spark, and ADLS Gen2.
Collaborate with the Data Architect to align platform implementation with enterprise data models, domain definitions, and governance standards.
Own and facilitate the code review process, ensuring all production code meets quality, performance, and maintainability standards.
Establish and enforce data quality frameworks, including validation, monitoring, alerting, and SLA adherence across pipelines and data products.
Oversee the end-to-end design, development, and operation of scalable ETL and streaming data pipelines on Azure Databricks, leveraging PySpark, Spark SQL, Delta Lake, and Databricks Workflows.
Drive the development of reusable, metadata-driven ingestion frameworks and modular data transformation patterns.
Troubleshoot and resolve complex platform, infrastructure, and pipeline issues, ensuring minimal downtime and optimal performance.