Data Engineering Senior Manager

IL LLP•Chicago, IL

3d•Hybrid

About The Position

The Data Engineering Senior Manager leads the design, development, and optimization of scalable data platforms supporting 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 put the Firm’s data strategy into practice while maintaining rigorous data privacy and governance standards. The position reports to the Data Platform and Engineering Director. It includes hiring, developing, coaching, and supervising direct reports, as well as conducting annual performance reviews. Applicants must be authorized to work in the United States without current or future employer sponsorship. The target salary range for the role in Illinois is $148,200–$211,200; salary varies by location and other factors. The compensation package includes bonus eligibility and a comprehensive benefits program.

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 the ability to set modeling standards and guide architect-level staff.
Skill in communicating data narratives through tables, reports, dashboards, and other visualization tools.
Demonstrated leadership in building, mentoring, and scaling high-performing, cross-functional data engineering teams in a dynamic environment.
Experience building and scaling data platforms in a major cloud environment (AWS, Azure, or GCP), with a focus on scalability, security, and performance.
Experience leading large-scale data migrations and consolidating disparate systems into a single source of truth for enterprise analytics.
Strong organizational skills, attention to detail, judgment, interpersonal communication, analytical and problem-solving skills.
Ability to work harmoniously and effectively with others, preserve confidentiality and exercise discretion, work under pressure, and manage multiple projects with competing deadlines and priorities.
Authorization to work in the United States without employer sponsorship now or in the future.

Nice To Haves

Master’s degree in Computer Science or 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 tools 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 aligned with business objectives and high standards for data quality, security, and performance.
Break down high-level data strategy and roadmaps 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 on modeling standards and best practices.
Lead development and continuous improvement of robust, business-aligned data platforms and pipelines, championing cloud-native services, distributed computing, orchestration, and real-time streaming.
Consolidate fragmented and siloed 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, dependable execution.
Design and implement processes and tools that provide data visibility and observability and turn raw data into actionable, trusted insights.
Strengthen data governance to safeguard data integrity and accessibility and comply with applicable regulations such as GDPR and CCPA.
Communicate technical concepts and progress to technical and non-technical stakeholders, keeping work aligned with broader data and technology goals.
Evaluate third-party tools, services, and vendors and recommend options aligned with the firm’s technical direction and budget.
Hire, develop, coach, and supervise direct reports, and conduct annual performance reviews.