job summary:
Role OverviewThe Data Engineering Manager will lead a team of approximately 7-9 data engineers/consultants supporting the continued delivery of Sidley's enterprise Databricks data platform.The platform architecture and core infrastructure are largely established. This role is therefore less about defining architecture from scratch and more about technical execution, data transformation, team leadership, and delivery. The manager will work closely with Data Architecture and BI to ensure data is appropriately ingested, transformed, and prepared for downstream reporting and dashboards.What We Are Looking ForPlease prioritize candidates with: - Strong data engineering fundamentals: 5+ years of hands-on data engineering experience with pipelines, ETL/ELT, data transformation, and modern cloud data platforms. - Recent technical depth: This person will not be expected to write production code regularly, but must still be close enough to the technology to read and review code, assess quality, understand architecture, troubleshoot issues, and provide credible technical direction. - People leadership: Ideally 2-4 years of recent mentorship/management experience. Strong Lead-level candidates with meaningful people-management experience may also be considered. - Leadership presence: We need someone assertive and confident enough to drive execution, hold engineers and consultants accountable, resolve conflict, manage performance, and keep the team moving. Communication skills and leadership presence will be important throughout the interview process.Think of the role as approximately 60% technical / 40% leadership, while recognizing this is ultimately a people-management position.TechnologyAzure Databricks specifically is NOT required. Please do not screen out strong candidates simply because their experience is on another cloud or comparable data platform. Strong experience with Databricks, Snowflake, or similar modern data platforms is relevant. Candidates should understand modern data architecture and be comfortable with technologies/concepts such as Python, SQL, ETL/ELT, Lakehouse architecture, CI/CD, testing, and data quality. Azure experience is preferred, but strong AWS/Google Cloud Platform cloud candidates should absolutely be considered.
location: Chicago, Illinois
job type: Permanent
salary: $165,000 - 185,000 per year
work hours: 9am to 4pm
education: Bachelors
responsibilities:
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. This role reports to the Senior Manager of Data Platform & Engineering.
Duties and 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.
Education and/or Experience: Required:
- 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, Delta Lake, Databricks SQL, Apache Spark, Unity Catalog, Databricks Workflows, or similar data platforms.
- Proficiency with Python 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 Google Cloud Platform, with Azure strongly preferred.
- Strong communication and stakeholder management skills, with the ability to translate between technical and business contexts.
Preferred:
- 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.
Other Skills and Abilities: The following will also be required of the successful candidate:
- 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
qualifications:
Education and/or Experience:
Required:
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, Delta Lake, Databricks SQL, Apache Spark, Unity Catalog, Databricks Workflows, or similar data platforms.
Proficiency with Python 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 Google Cloud Platform, with Azure strongly preferred.
Strong communication and stakeholder management skills, with the ability to translate between technical and business contexts.
Preferred:
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
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