Senior Manager, Data Engineering

About Sterling Brokers

Sterling Capital Brokers is a fast-growing Canadian employee benefits brokerage built on proprietary technology. We help employers design, deliver, and manage group benefits programs that produce real outcomes for their people. We move quickly, operate with a high degree of ownership, and build solutions that are purpose-fit for our clients rather than borrowed from a bigger playbook.

Data is genuinely strategic here — not an afterthought. Our challenge is the one most growing companies eventually hit: the data exists, but access to it doesn’t. We are moving deliberately from raw visibility toward governed, role-based, self-serve access that lets every team make better decisions faster. This role leads that work.

About the role

This is a player/coach role for a hands-on data engineering leader. You will own the architecture, delivery, and strategic direction of our data platform while leading a small, high-performing team — today one full-stack data engineer, one analytics engineer, and a program manager. You will grow that team deliberately as the business scales, with a bias toward leverage: building tools, patterns, and platforms that multiply the team’s output rather than adding headcount in lockstep with demand.

Expect to split your time roughly 50/50 between building and leading. In a given week you may design a Unity Catalog governance model, review a teammate’s pipeline PR, write production code on a hard problem yourself, run your 1:1s, and present a recommendation to the executive team. If you want a role that is purely managerial, or purely individual-contributor, this is not it — and we say that plainly so the right person self-selects in.

You will also serve as connective tissue between our internal technology function and our client experience team, keeping collaboration pragmatic, data-informed, and focused on outcomes.

How we build: We run a Databricks lakehouse on AWS — medallion architecture, Unity Catalog as our governance control plane, dbt for modeling, and Databricks Workflows plus Airflow for orchestration. Everything ships through CI/CD, and we’re moving self-serve analytics onto Databricks-native tooling like Genie. We favour governed, well-documented, reusable data over one-off pipelines.

What you'll do

Lead and Build the Team (the “coach”)

- Lead and develop the data engineering team with clear direction, regular 1:1s,candid performance feedback, and real growth opportunities.

- Grow the team deliberately and non-linearly — hire for leverage and invest in tooling and automation so output scales faster than headcount.

- Set technical standards and raise the bar through code review, design review, and pairing — modeling the engineering quality you expect.

- Build a team that documents extensively and creates way finding paths to that documentation, so the rest of Sterling can discover what we build and why.

Architect and Engineer the Platform (the “player”)

- Design, build, and maintain scalable, reliable pipelines on Databricks — through a medallion architecture, into well-modeled gold-layer tables.

- Stay hands-on in the codebase: write and review production Python and SQL, untangle messy source data into reusable, documented data models, and debug across the stack when it matters.

- Own orchestration and reliability across Databricks Workflows and Airflow —performance, cost, observability, and uptime of the data environment.

- Drive the near-term roadmap across three surfaces: self-serve analytics that democratize access for internal teams, embedded client-facing data products, and ML/AI enablement (feature pipelines and the data foundation for advanced analytics).

- Close the documentation and governance gaps that block trust in the data —column-level definitions, decoded business semantics, table lineage, and freshness/quality signals.

Strategy and Business Alignment

- Own the data roadmap and tie it tightly to company strategy and measurabl