Senior Manager, Data Engineering

Sterling Brokers

19d•CA$130,000 - CA$170,000•Remote

About The Position

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. 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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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 intooling 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.