Data Engineering Senior Manager

Sidley Austin•Chicago, IL

4d•$148,200 - $211,200•Hybrid

About The Position

Reporting to the Data Platform and Engineering Director, the Data Engineering Senior Manager leads the design, development, and optimization of scalable data platforms that support firmwide analytics, machine learning, and AI. This hands-on leadership role sets technical direction, develops a high-performing engineering team, and delivers secure, reliable, high-performing data products. The role partners with AI, business intelligence, product, operations, and support colleagues to turn the Firm’s data strategy into working systems while maintaining rigorous data privacy and governance standards. The successful candidate will translate strategic goals into prioritized, sprint-ready work; lead data architecture, modeling, platforms, and pipelines; and consolidate siloed data into trusted enterprise sources. They will also strengthen data observability and governance, communicate progress to technical and non-technical stakeholders, assess third-party tools and vendors, and hire, develop, coach, and supervise direct reports.

Requirements

Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field.
At least 10 years of experience in data engineering.
At least 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 such as dbt and workflow orchestration such as Airflow.
Strong conceptual and logical data modeling skills, including ability to set modeling standards and guide architect-level staff.
Skill in creating data narratives through tables, reports, dashboards, and other visualization tools.
Demonstrated ability to build, mentor, and scale 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.
Strong organizational skills.
Strong attention to detail.
Good judgment.
Strong interpersonal communication skills.
Strong analytical and problem-solving skills.
Ability to work harmoniously and effectively with others.
Ability to preserve confidentiality and exercise discretion.
Ability to work under pressure.
Ability to manage multiple projects with competing deadlines and priorities.
Must be authorized to work in the United States without employer sponsorship, now or in the future.

Nice To Haves

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

Responsibilities

Translate the Firm’s data strategy into execution, delivering initiatives that maintain data quality, security, and performance and align with business objectives.
Break down the data strategy and roadmap into scoped epics, user stories, and sprint-ready deliverables, and drive execution and prioritization through the team’s Jira backlog.
Own implementation and ongoing enhancement of scalable data architectures supporting analytics, machine learning, and AI.
Shape conceptual and logical data models, contribute hands-on, and provide technical direction and mentorship to data architects on modeling standards and practices.
Lead development and continuous improvement of business-aligned data platforms and pipelines, using modern tooling such as cloud-native services, distributed computing, orchestration, and real-time streaming.
Consolidate fragmented data into a trusted source of truth for enterprise analytics and reporting.
Partner with AI, BI, Product, Operations, and support teams to align priorities and deliver integrated execution.
Design and implement processes and tooling for data visibility and observability, turning raw data into actionable, trusted insight.
Strengthen data governance to protect data integrity and accessibility and support compliance with regulations such as GDPR and CCPA.
Communicate technical concepts and progress to technical and non-technical stakeholders and align work with broader data and technology goals.
Evaluate third-party tools, services, and vendors and recommend options suited to the firm’s technical direction and budget.
Hire, develop, coach, and supervise direct reports, and conduct annual performance reviews.