Job description

Reporting to the Data Platform and Engineering Director, the Data Engineering Senior Manager leads the design, build, and optimization of the scalable data platforms that power analytics, machine learning, and AI across the firm. As a hands-on leadership role, it will set technical direction, grow a high-performing engineering team, and deliver the secure, reliable, high-performing data products that make better decisions possible firmwide. Partnering closely with colleagues in AI, business intelligence, product, and operations, this role will turn the Firm’s data strategy into working systems while upholding rigorous data privacy and governance standards. Duties and Responsibilities: Translate the Firm’s data strategy into execution, delivering initiatives that hold data quality, security, and performance to a high standard and keep them aligned with business objectives. Decompose high-level data strategy and roadmap into clearly scoped epics, user stories, and sprint-ready deliverables, driving execution and prioritization through the team’s Jira backlog. Own the implementation and ongoing enhancement of scalable data architectures that power analytics, machine learning, and AI capabilities. Shape the firm’s conceptual and logical data models, contributing hands-on while providing technical direction and mentorship to data architects on modeling standards and best practices. Lead the development and continuous evolution of robust, business-aligned data platforms and pipelines, championing modern tooling such as cloud-native services, distributed computing, orchestration, and real-time streaming. Drive the consolidation of fragmented, siloed data into a single, trusted source of truth that powers enterprise analytics and reporting. Partner with teams across AI, BI, Product, Operations, and support to align on priorities and deliver integrated, dependable execution. Design and implement the processes and tooling that give the firm clear data visibility and observability, turning raw data into actionable, trusted insight. Strengthen data governance practices that safeguard data integrity, accessibility, and compliance with applicable regulations such as GDPR and CCPA. Communicate technical concepts and progress clearly to both technical and non-technical stakeholders, keeping work aligned with broader data and technology goals. Evaluate third-party tools, services, and vendors, recommending choices that fit the firm’s technical direction and budget. undefined Hire, develop, coach, and supervise direct reports, and conduct annual performance reviews. Education and/or Experience: Required: Bachelor s degree in Computer Science, Engineering, Data Science, or a related field A minimum of 10 years of experience in data engineering A minimum of 3 years in a technical leadership or team management role Advanced knowledge of big-data architecture and distributed processing, including tools such as Spark and Kafka and Lakehouse or Data Lake patterns Advanced proficiency in SQL and Python, with strong command of ELT/ETL tooling (e.g., dbt) and workflow orchestration (e.g., Airflow) Strong conceptual and logical data modeling skills, with the ability to set modeling standards and guide architect-level staff Skilled at crafting compelling data narratives through tables, reports, dashboards, and other visualization tools Demonstrated leadership abilities in building, mentoring, and scaling high-performing, cross-functional data engineering teams in a dynamic environment Proven experience building and scaling data platforms in a major cloud environment (AWS, Azure, or GCP) with a focus on scalability, security, and performance Proven experience leading large-scale data migrations and consolidating disparate systems into a single source of truth for enterprise analytics Preferred: Master s degree in Computer Science, Engineering, or MBA Experience implementing and operating Databricks at scale Familiarity with data cataloging and governance tooling (e.g., Unity Catalog, Collibra) and with data-observability platforms Working knowledge of CI/CD and infrastructure-as-code (e.g., Terraform) for application and infrastructure deployment Experience delivering the data infrastructure behind machine learning and AI workloads, such as feature pipelines and model-ready datasets Experience working in an Agile delivery model undefined Other Skills and Abilities: The following will also be required of the successful candidate: Strong organizational skills Strong attention to detail Good judgment Strong interpersonal communication skills Strong analytical and problem-solving skills Able to work harmoniously and effectively with others Able to preserve confidentiality and exercise discretion Able to work under pressure Able to manage multiple projects with competing deadlines and priorities #LI-OE1 #LI-Hybrid Applicants must be authorized to work in the United States without the need for employer sponsorship, now or in the future The target salary range for this role is: $148,200 - $211,200 if located in Illinois. Salaries vary by location and are based on numerous factors, including, but not limited to, the relevant market, skills, experience, and education of the selected candidate. Our compensation package also includes bonus eligibility and a comprehensive benefits program. Benefits information can be found at Sidley.com/Benefits . To perform this job successfully, an individual must be able to perform the Duties and Responsibilities above satisfactorily and meet the requirements. The requirements listed above are representative of the minimum knowledge, skill, and/or ability required. Reasonable accommodations will be made to enable individuals with disabilities to perform the essential functions of the job. If you need such an accommodation, please email sidleytalentacquisition sidley.com (current employees should contact Human Resources). Sidley Austin LLP is an Equal Opportunity Employer.

Requirements

Must-haves

Translate the Firm’s data strategy into execution, delivering initiatives that hold data quality, security, and performance to a high standard and keep them aligned with business objectives.
Decompose high-level data strategy and roadmap into clearly scoped epics, user stories, and sprint-ready deliverables, driving execution and prioritization through the team’s Jira backlog.
Own the implementation and ongoing enhancement of scalable data architectures that power analytics, machine learning, and AI capabilities.
Shape the firm’s conceptual and logical data models, contributing hands-on while providing technical direction and mentorship to data architects on modeling standards and best practices.
Drive the consolidation of fragmented, siloed data into a single, trusted source of truth that powers enterprise analytics and reporting.
Partner with teams across AI, BI, Product, Operations, and support to align on priorities and deliver integrated, dependable execution.
Design and implement the processes and tooling that give the firm clear data visibility and observability, turning raw data into actionable, trusted insight.
Strengthen data governance practices that safeguard data integrity, accessibility, and compliance with applicable regulations such as GDPR and CCPA.
Communicate technical concepts and progress clearly to both technical and non-technical stakeholders, keeping work aligned with broader data and technology goals.
Evaluate third-party tools, services, and vendors, recommending choices that fit the firm’s technical direction and budget.
Hire, develop, coach, and supervise direct reports, and conduct annual performance reviews.
Bachelor s degree in Computer Science, Engineering, Data Science, or a related field
A minimum of 10 years of experience in data engineering
A minimum of 3 years in a technical leadership or team management role
Advanced knowledge of big-data architecture and distributed processing, including tools such as Spark and Kafka and Lakehouse or Data Lake patterns
Advanced proficiency in SQL and Python, with strong command of ELT/ETL tooling (e.g., dbt) and workflow orchestration (e.g., Airflow)
Strong conceptual and logical data modeling skills, with the ability to set modeling standards and guide architect-level staff
Skilled at crafting compelling data narratives through tables, reports, dashboards, and other visualization tools

Nice-to-haves

Master s degree in Computer Science, Engineering, or MBA
Experience implementing and operating Databricks at scale
Familiarity with data cataloging and governance tooling (e.g., Unity Catalog, Collibra) and with data-observability platforms
Working knowledge of CI/CD and infrastructure-as-code (e.g., Terraform) for application and infrastructure deployment
Experience delivering the data infrastructure behind machine learning and AI workloads, such as feature pipelines and model-ready datasets
Experience working in an Agile delivery model

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaijiraaccessibilitysqlpythonawsazuregcpci/cdagile

Platform: workday
Effort: long