Senior Manager, Data Engineering

About the role

This role partners with leaders across energy, financial services, government, and consumer sectors to deliver data, AI, and engineering solutions. You will define and implement on-premise or cloud architectures that support digital transformation and enable confident, certain delivery at scale. In this capacity, you act as a technical leader responsible for designing the data platforms that underpin critical client initiatives. The position requires a blend of strategic vision and hands-on delivery to ensure solutions are both robust and future-proof. You will guide teams in selecting the right tools and patterns to meet complex business requirements. Success in this role is measured by your ability to turn ambiguous challenges into concrete, executable data strategies. You will be expected to mentor others while maintaining a high standard of engineering excellence.

Key facts

What you'll do

Define and implement on-premise or cloud architectures, such as cloud data warehouses, data lakes, or data platforms, to enable digital transformation for clients.

Use these architectures to build and maintain operational ETL/ELT data pipelines across multiple sources and construct relational and dimensional data models for analytics.

Perform maturity assessments of clients' data capabilities and recommend improvements aligned with their strategy, policies, and standards.

Build technology blueprints and advise clients on available technology options to address their specific business needs.

Apply strong hands-on expertise in data engineering, delivering robust and scalable data architectures and pipelines using modern tools such as Databricks.

Apply experience with cloud technologies on Azure, AWS, and GCP as infrastructure and services, and on big data platforms in on-premise or cloud setups.

Use knowledge of DevOps practices in data engineering and build CI/CD pipelines for data workloads.

Write competent SQL and code in at least one modern programming language such as Python.

Understand core concepts including distributed computing, batch and stream processing, and how pipelines are built and deployed in the cloud with attention to schedules and SLAs.

Bring line management or team leadership experience, ideally across multiple geographical locations.

Show a proven ability to work toward formal sales targets and bring in new clients in a consulting environment.

Requirements

The posting states a bachelor's degree requirement. You must demonstrate strong hands-on expertise in data engineering, delivering robust and scalable data architectures and pipelines using modern tools such as Databricks.

You are required to apply experience with cloud technologies on Azure, AWS, and GCP as infrastructure and services, and on big data platforms in on-premise or cloud setups.

You must use knowledge of DevOps practices in data engineering and build CI/CD pipelines for data workloads.

You must write competent SQL and code in at least one modern programming language such as Python.

You must understand core concepts including distributed computing, batch and stream processing, and how pipelines are built and deployed in the cloud with attention to schedules and SLAs.

You must bring line management or team leadership experience, ideally across multiple geographical locations.

You must show a proven ability to work toward formal sales targets and bring in new clients in a consulting environment.

Practical notes

Applications undergo review by a Talent Acquisition team member, and hiring decisions are not made solely through automated screening or AI tools.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.

Questions to ask

Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.