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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 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. Governance, Security, and Compliance 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. What We're Looking For Leadership Experience — Required 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. Technical Depth — Required 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. Business Acumen — Non-Negotiable 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 Have 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. What Will Help You Succeed Here 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 judgm
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.