Senior Manager, Data Engineering

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.

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.

Own the data roadmap and tie it tightly to company strategy and measurable business outcomes.

Translate business questions into data work and back again — and explain to the CTO and executive team not just what you built, but why it matters and what it unlocks.

Proactively surface opportunities in our data that inform commercial decisions, product direction, and client experience — e.g., which clients are at risk, where our best clients come from.

Establish and enforce data governance — access controls, anonymization (up to and including differential-privacy techniques where warranted), and lineage —using Unity Catalog as the control plane.

Build practices that hold up to our compliance posture: SOC 2 Type 2 (currently in our audit observation window), PIPEDA, member PII protection, and applicable insurance regulations. You will treat this as mission-critical, “real-money” infrastructure, because it is.

You have led a data or engineering team before. This is not a first-time-manager seat.

You are comfortable running 1:1s, writing performance reviews, navigating team dynamics, and developing people.

Senior Manager level at an enterprise or Director level at a smaller organization is the experience benchmark.

Crucially, you have stayed technical while leading — you did not stop writing or reviewing code when you started managing, and you don’t want to.

Hands-on experience designing and building data pipelines, warehouses/lakehouses, and analytical infrastructure at scale.

Strong Python (applying software-development best practices) and strong SQL —querying, views, and reusable data models built from often-messy sources.

Production experience with Databricks (or a directly comparable Spark-based lakehouse) and a data-cataloging/governance layer such as Unity Catalog.

Experience building ETL/ELT pipelines and running them on orchestrators —Databricks Workflows and Airflow specifically, or close equivalents.

Proficiency with dbt for data modeling and transformation — building modular, tested, version-controlled models.

Hands-on experience in AWS as a cloud environment.

Experience implementing CI/CD for data pipelines and infrastructure — automated testing, deployment, and version control for data workflows.

A working grasp of data governance, access controls, and compliance frameworks for regulated data (PII, SOC 2).

Able to move from writing a technical spec to presenting a strategic recommendation in the same week.

You understand how a business creates value over time, and you use data to sharpen that understanding — not just to answer the question that was asked.

You can sit in a room with a CEO and explain what you built, why it matters strategically, and what it will unlock.

Your communication is clear in writing and out loud. Technical complexity is never an excuse for unclear communication.

Statistics background and experience with R; familiarity with ML workflows, AutoML, and notebook environments (Databricks, Jupyter, or commercial equivalents).

Experience making data self-serve and accessible to non-technical teams —semantic modeling, natural-language query (e.g., Databricks Genie), and Databricks-native dashboards. Background with Power BI or Tableau is useful context, though we are moving off both.

Experience integrating third-party services via API (e.g., OCR/Textract-type ingestion).

Experience with mission-critical or “real-money” systems and multidimensional data.

Experience in a regulated industry — financial services, insurance, or healthcare —is a strong asset. It accelerates your grasp of our regulatory environment and why client trust is not optional. It is not a deal-breaker for an exceptional candidate with strong business acumen and a track record of fast learning.

You have operated in smaller, faster-moving environments where resourcefulness, speed, and judgment matter as much as technical rigour — and you can set direction without heavy process scaffolding.

You bring new thinking. You ask whether the industry convention is right for us rather than defaulting to it.

You are comfortable with ambiguity and know when to escalate versus when to simply decide.

High visibility and direct impact — you work closely with executive leadership and shape decisions that matter.

A team that moves fast and trusts its people to own their work.

The chance to build something, not just maintain it — you are setting the foundation for how a growing company uses its data.

A business where data is genuinely strategic, and a leadership team that treats it that way.

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.