Lead Data Engineer

MTN Industrial

. Manage, coach and develop the data engineer on your team, with regular one to ones and clear goals.

Posted 10/31/2026full-timeRemote • CanadaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in data engineering, particularly with Databricks, SQL, and Python, while effectively managing and mentoring a team. Capable of building reliable data pipelines and customer-facing data products, ensuring compliance with data governance and privacy standards.

Highest-signal resume keywords

Data Engineering LeadershipDatabricks Lakehouse ManagementPipeline Development and OptimizationAI Tool ImplementationData Governance and Privacy Compliance

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringDatabricksSQLPythonPipeline DevelopmentData ModellingData Quality AssuranceData GovernanceAI IntegrationEmbedded Analytics

Soft Skills

Team ManagementCoachingMentoringAutonomyCollaboration

Tools & Technologies

SalesforceNetSuiteSigmaJiraGitHub

Industry Keywords

FintechPaymentsRegulated EnvironmentsData PrivacyGDPR

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

Manage, coach and develop the data engineer on your team, with regular one to ones and clear goals.
Set standards for code review, testing, documentation and data on-call.
Plan the team's work with Engineering, Finance and Product.
Help shape the team as it grows, including hiring if and when the time comes.
Own Trolley's Databricks lakehouse end to end: ingestion, modelling, quality and cost.
Build and run pipelines from the production platform, Salesforce, NetSuite and third-party sources.
Keep internal reporting in Sigma accurate, fast and self-serve.
Make pipelines observable and recoverable.
Run data behind month-end close, revenue calculations and vendor cost reconciliation.
Support audits and year-end reconciliation with trusted data.
Build and improve customer-facing data products, including the data connector and embedded reporting.
Work with Engineering and Product on fraud and risk signal features.
Build data foundations for safe AI tools and agents.
Use AI to build, test and document pipelines faster.
Identify and demonstrate product and workflow improvements enabled by data and AI.
Bring tools such as Jira and GitHub into the warehouse to measure engineering delivery.
Apply data management and privacy policies, partnering with InfoSec and Compliance on access, retention and audit requirements.

Requirements

What you’ll need

7+ years in data engineering, with ownership of a production data platform.
Experience leading other engineers: managing, mentoring or leading a small team day to day.
Deep experience with Databricks or a similar lakehouse, SQL, and Python.
Experience building reliable pipelines from operational databases and SaaS systems.
Experience supporting finance or accounting processes with data.
Hands-on use of AI tools in your own engineering work, and clear thinking about where AI helps and where it doesn't.
Comfortable as the most senior data person in the company, with a lot of autonomy.
Formal people management, and interest in growing a team over time.
Fintech, payments or other regulated environments.
Customer-facing data products, embedded analytics or data sharing.
Building context layers, retrieval or data pipelines for AI agents.
Data governance and privacy in practice: PII handling, access control, GDPR.

Benefits

Comp & perks

Competitive salary
ESOP Program: Meaningful equity and ownership that aligns your success with ours
Extended healthcare benefits from Day 1.
4 weeks paid vacation per year + public holidays (+ 1 extra vacation day per year of service).
30 days remote work from anywhere.
Gym membership allowance.
1/2 day Fridays off during summer.
Company-provided use of Apple MacBook and Mac gear.