Staff Data Engineer

Purpose Unlimited is an independent financial services company with an unrelenting focus on customer-centric innovation, delivered through technology-driven solutions. Led by entrepreneur Som Seif, the company is developing a diversified product platform aimed at addressing historically underserved segments of the market. Purpose Unlimited’s businesses include Purpose Investments, Advisor Solutions by Purpose, and Driven.

Vacancy Status: This is for a current opening.

Responsibilities: What you will own

Data is the foundation every AI system at Purpose is built on. This role owns that foundation.

You will join a high-output engineering team and operate at the senior end of that bar, owning the pipelines, data models, and platform infrastructure that data scientists, analysts, AI systems, and downstream products depend on for decisions that move real money and shape real client outcomes. Your output isn't a dashboard or a model. It's the layer of trust everything else stands on. Purpose Unlimited is building the infrastructure for a new kind of financial services firm, one where AI and human judgment operate in combination, and where data quality is not a nice-to-have but a competitive asset. This role is central to that bet.

You'll work closely with data scientists, software engineers, and product teams to understand what data is needed, where it lives, and how to make it accurate, observable, and durable at scale, including the data pipelines that feed our AI systems and the models our advisors and clients will rely on.

We use Object Process Modeling (OPM) for system mapping and Figures-of-merit (FOMs) as our measurement layer. You don't need to be a process mapping expert. You do need to think in systems, understanding how data flows, where it degrades, and how platform decisions made today constrain what AI systems can do tomorrow.

What AI does so you don’t have to

AI tools handle a growing share of the mechanical work in this role. You'll use them fluently, not as a novelty, but as a multiplier.

• Pipeline scaffolding and boilerplate - AI-assisted development tools generate initial pipeline structures, dbt model skeletons, and transformation logic from natural language prompts. You review, tune, and govern.

• First-pass data quality rules - AI suggests validation checks based on schema analysis and historical patterns. You own the standards, not the typing.

• Standard documentation - AI generates first-draft data dictionary entries, model descriptions, and lineage docs. You enrich and validate.

• Routine incident triage - observability tooling auto-diagnoses common data quality patterns and proposes root causes. You handle the edge cases and cross-system decisions that require judgment.

What only you can do

• Design data architecture that is trustworthy enough to feed AI decisions in a regulated financial environment, where a broken pipeline isn't just an inconvenience, it's a compliance event.

• Hold the line on data quality when everyone else wants to ship faster, and trace failures to the root cause rather than patching downstream symptoms.

• Operationalize the gap between a data scientist's working notebook and a production-grade, monitored, governed scoring system.

• Govern AI-generated pipeline code - reviewing, validating, and setting the standard for what makes AI-assisted data engineering production-safe at Purpose.

• Build the institutional trust that makes analysts and data scientists say 'I can rely on this number' and mean it.

What you’ll own

• Reliable data infrastructure: pipelines, models, and serving layers that ingest, transform, and deliver data from operational systems - CRM, marketing automation, financial platforms, event streams - are accurate, observable, and production-grade. When something breaks downstream, you trace it to the source.

• A governed data model: dimensional models, semantic layers, and governed datasets that are self-service-ready, version-controlled, and trustworthy enough that analysts and AI systems can both build on them without second-guessing the numbers.

• Data quality end-to-end: validation, monitoring, and observability that catches issues at the source before they reach a dashboard, a model, or a client-facing decision. Incidents are rare; when they occur, root cause is identified and remediated at the model layer, not masked downstream.

• AI pipeline operationalization: the feature pipelines, serving infrastructure, and scheduling that turn data science experiments into durable, monitored, production scoring systems - with drift detection, alerting, and documented lineage.

• Platform governance: warehouse configuration, orchestration, compute optimization, access controls, and cost management - evolving as Purpose’s AI and data science investments grow. You set the standard for what AI-assisted code looks like before it enters the production stack.

• Engineering discipline as default: version control, CI/CD, testing, documentation, and code review are not additions to the role, they are the role. This applies equally to human-authored and AI-generated code.

What you must bring

• Deep fluency in SQL and Python, designing and maintaining complex transformation logic at scale, with production-grade testing and documentation standards.

• Hands-on experience building and operating production data pipelines using modern tooling: dbt, Airflow or equivalent orchestration, and at least one cloud data warehouse (Snowflake, BigQuery, or Redshift).

• Strong data modeling foundations - dimensional modeling, slowly changing dimensions, schema design that balances flexibility with query performance.

• Meaningful experience in a regulated industry (financial services, healthcare, or equivalent) — you understand what SOC 2 or ISO 27001 compliance means for how pipelines are built, accessed, audited, and controlled.

What will set you apart

• Experience governing or reviewing AI-assisted code in a production data environment - you've thought carefully about what makes AI-generated pipeline logic safe to deploy.

• Familiarity with ML operationalization - feature pipeline design, serving infrastructure, and monitoring for deployed models.

• Exposure to LLM data infrastructure: RAG pipelines, vector stores, or embedding workflows. Not required - but this is where the role is heading.

• Strong opinions about data contracts and how to formalize interfaces between upstream systems and the data platform.

• A track record of raising the data quality bar on an existing team, not just building from a blank page, but inheriting a system and improving its reliability and trustworthiness.

• You’ve used AI-assisted development tools (GitHub Copilot, Cursor, Claude Code, or equivalent) as a genuine productivity multiplier, not just experimented with them.

Example problems you’ll work on

• Our CRM, marketing automation, and financial systems each carry their own concept of a ‘client’ - how do we resolve identity and build a governed single source of truth that analysts and AI models can both trust?

• A data scientist has a working propensity model in a notebook, what does it take to make it a reliable, monitored, production scoring job with proper feature pipelines, drift detection, and auditability?

• Pipeline failures are discovered downstream by analysts hours after the fact - how do we build observability that catches data quality issues at the source, before they reach a dashboard or a client-facing decision?

• We have ten dashboards that each report slightly different revenue numbers - where does the disagreement live, and how do we fix it at the model layer so it can’t happen again?

• We’re integrating an LLM into an advisor-facing tool that retrieves client information to support recommendations - what does the data infrastructure layer look like, and how do we ensure the retrieval layer is accurate, governed, and auditable?

What success looks like in your first year

• The data platform has measurably improved in reliability - pipeline failure rates are down, mean time to detection on data quality issues is under 4 hours, and the root cause of recurring incidents has been addressed at the model layer.

• At least two data science experiments have been operationalized into production-grade scoring systems, with monitoring, documentation, and data lineage that any engineer on the team can maintain.

• The team’s data modeling standards have been elevated - governed datasets that data scientists and AI systems can build on with confidence, and a semantic layer that reduces ad hoc interpretation.

• AI-assisted development has been adopted as a standard practice on the data team, with clear internal guidelines for what AI-generated pipeline code looks like before it enters production.

• You’ve become the engineer that analysts and data scientists come to when they can’t trust a number, and your response is consistent: trace it to the source, fix it at the root, prevent recurrence.

Every role at Purpose is assessed against our five values:

• Innovation – You bring new ideas, challenge the status quo, and find better ways of building. You don’t wait for permission to experiment.

• Courage – You speak up when something isn’t working, take difficult stands on technical quality, and act with conviction even when it’s uncomfortable.

• Owner’s Mindset – You take full accountability for your squad’s outcomes. You treat the platform, the team, and the business as your own.

• Client Focus – You place client outcomes at the center of every architectural and delivery decision.

• Winning Through Individual and Collective Growth – You invest in your own development and actively contribute to the growth of every engineer around you.

Why should you join us?

• We are one of Canada's Top Small & Medium Employers' 2023, 2024, 2025 & 2026.

• We believe in innovation and a vibrant culture - work for an innovative, people-first, financial services firm that values entrepreneurialism.

• We believe in a flexible work structure – A flexible hybrid work model that empowers you to do your best work whether at home or the office.

• We care about your rewards - Competitive compensation including equity program.

• We care about your health – comprehensive group health and dental benefits and life insurance at little to no cost to you. We also offer a Lifestyle Spending Account for all your wellness needs.

• We care about your quality of life - flexible paid time-off policy with unlimited vacation days, and flexible sick and mental health days.

• We care about your family - Paid parental leave for eligible employees with a top-up.

• We care about your future – Generous Group RRSP matching and an optional TFSA program.

• We care about your development – We offer training opportunities and tuition support year-round.

Purpose Unlimited is an equal opportunity employer and we are dedicated to fostering an inclusive and barrier-free work environment for all employees and candidates. We encourage all qualified candidates to apply and if accommodation is required during any stage of the recruitment process, please contact any member of the People & Culture team at PeopleCultureTeam@purpose-unlimited.com. We thank all applicants for their interest; however, only those selected for interviews will be contacted.

Our work philosophy is a hybrid model allowing for flexibility and collaboration. Applicants must be legally entitled to work in Canada. Immigration sponsorship is not offered for this role.

We may use artificial intelligence technology to assist in screening, assessing, or selecting applicants for this position. Final hiring decisions are made by qualified human reviewers.