Senior Data Engineer
Full Time 100 - 130 CAD per-year-salaryJob Overview
At Trackforce, we are transforming physical security operations that are managed around the world. As the leading SaaS platform for physical security workforce management, we provide security companies and organizations with a streamlined solution to manage their guard forces, respond faster, operate more efficiently, and reduce costs — all while staying focused on safety and protection.
We support 4,600+ clients across more than 50 countries and are proud to be a growing team of 300+ professionals. With our headquarters in Dallas, Texas, and Centers of Excellence in Montreal, Quebec and Wroclaw, Poland, we collaborate across regions and time zones in a hybrid work environment that combines flexibility, connection, and meaningful impact.
At Trackforce, we operate in both hybrid and remote models, offering flexibility to balance in‑office collaboration with WFH. This approach allows our teams to stay connected while maintaining autonomy and work‑life balance. We are highly focused on delivering value to our customers, and our recent merger has strengthened our position as the market leader in security workforce management software.
At Trackforce, we are transforming physical security operations that are managed around the world. As the leading SaaS platform for physical security workforce management, we provide security companies and organizations with a streamlined solution to manage their guard forces, respond faster, operate more efficiently, and reduce costs — all while staying focused on safety and protection.
We support 4,600+ clients across more than 50 countries and are proud to be a growing team of 300+ professionals. With our headquarters in Dallas, Texas, and Centers of Excellence in Montreal, Quebec and Wroclaw, Poland, we collaborate across regions and time zones in a hybrid work environment that combines flexibility, connection, and meaningful impact.
At Trackforce, we operate in both hybrid and remote models, offering flexibility to balance in‑office collaboration with WFH. This approach allows our teams to stay connected while maintaining autonomy and work‑life balance. We are highly focused on delivering value to our customers, and our recent merger has strengthened our position as the market leader in security workforce management software.
Trackforce is seeking a highly skilled and motivated Senior Data Engineer to join our Data team. In this role, you will be responsible for designing, building, and maintaining the data infrastructure that powers our SaaS platform, enabling clients across the globe to manage their security workforces with speed, accuracy, and insight.
You'll design and ship the pipelines and data models that move multi-tenant data from our platform into a modern, query-ready foundation. And you'll help shape the technical direction alongside the engineering manager and the team as we define the strategy. This is a zero-to-one role for someone who wants to build the foundation, not inherit it.
This is a high-impact, technically deep role for an engineer who is passionate about scalable data systems, thrives in a collaborative environment, and is eager to help shape the data foundation of a growing global SaaS company.
WHAT YOU'LL BE DOING;
- 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.
- 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.
- Share knowledge and mentor teammates as the team grows; help set a high bar for engineering quality.
- 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.
- 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 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.