Trackforce - Senior Data Engineer
Requirements
• Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field (or equivalent hands-on experience). • 7+ years in data engineering, with strong SQL and relational data modeling on large datasets. • Proven experience designing and operating ETL/ELT pipelines into a data warehouse or data lake/lakehouse. • Hands-on with AWS data services (e.g., S3, Glue, Athena; DMS or similar CDC tooling) • Experience with distributed data processing (e.g., Spark/PySpark) and modern lakehouse table formats (e.g., Apache Iceberg, Delta), or a strong track record that transfers. • Comfortable with streaming/queue-based ingestion (e.g., Kinesis, SQS, Kafka) and building for scale and failure. • Solid grounding in data governance, quality, and compliance, ideally for multi-tenant SaaS data. • Proficiency in pipeline automation. • Clear communicator who ships pragmatically in ambiguity. • Nice to have: dbt or similar transformation frameworks; experience exposing client-facing data via APIs or governed data sharing; SaaS or workforce-management/physical-security domain exposure; familiarity with AI/ML data infrastructure patterns. • Success Metrics • Success in this role will be measured by the following outcomes within the first 3,6 and12 months: • First 90 days: ship a first working pipeline component end-to-end and, with the team, produce a prioritized plan for the data platform build-out. • 6 months: demonstrable improvements in pipeline reliability and processing performance; a repeatable pattern for onboarding new data domains. • 12 months: governance and quality monitoring established across core domains; at least one major, production-grade pipeline/lakehouse component powering a client-facing or analytics capability. • Trusted collaborator — strong cross-functional feedback on delivery, knowledge sharing, and raising the technical bar. • Our office is now located at 2020 Blvd Robert‑Bourassa, Suite 2000, Montreal, QC H3A 2A5, offering a more central and easily accessible workspace.
Responsibilities
• Data Architecture & Pipeline Development • Design, build, and operate scalable ETL/ELT pipelines that ingest, transform, and load multi-tenant data into a data warehouse/lakehouse. • Build for change data capture (CDC) and high-volume, near-real-time ingestion; tune pipelines for reliability, performance, and cost. • Help design the data models and lakehouse schema that make client-facing data accurate, performant, and secure to expose per tenant. • Contribute to the API-as-product effort — data access patterns, versioning, and documentation that clients depend on. • Quality, governance & security • Establish data quality monitoring, alerting, and observability across core data domains. • Implement multi-tenant data isolation and governance practices appropriate for enterprise, compliance-sensitive clients. • Act as a go-to for investigating and resolving data integrity and availability issues. • Collaborate & elevate • Partner across Product, Engineering, and DevOps to turn requirements into pragmatic data solutions. • Use AI-assisted development tools in your own workflow (pipeline code, query optimization, documentation) and share what works. • Build with an eye toward a data foundation that can later power analytics and AI/agent-based capabilities.
Benefits
• · Hybrid and flexible work model • · Three weeks of vacation starting in your first year • · Paid sick days & family obligation days • · Comprehensive health & dental coverage from Day 1 • · 24/7 telemedicine access • · Mental health & wellness support • · Life insurance, AD&D, long term‑ disability & critical illness coverage • · RRSP & DPSP with employer matching • · Employee referral bonus • · Paid volunteer day & recognition programs
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