Senior Analytics Engineer at Versaterm

, , Canada -

Full Time

Start Date

Immediate

Expiry Date

20 May, 26

Salary

0.0

Posted On

19 Feb, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

SQL, dbt, Jira, Confluence, Azure DevOps, GitHub, CI/CD, Aha!, APIs, Webhooks, Data Modeling, Power BI, Tableau, Looker, Python, ELT/ETL

Industry

Software Development

Description

The Company Versaterm is a global public safety solutions company helping agencies transform how they serve their communities. Since 1977, we’ve been building an ecosystem of intuitive tools designed for public safety agencies, forensic labs, court systems, schools and other institutions. Through purposeful integrations and a selective growth strategy, we focus on improving workflows to help our customers achieve more efficient operations, better service and more just outcomes. Our teams are driven by innovation, expertise and an unwavering commitment to customer success. As we continue to grow and expand our ecosystem, you’ll have the opportunity to contribute to solutions that enhance community safety and transform the future of public safety technology. If you’re passionate about making a meaningful difference, we’d love to hear from you. The Role We are seeking a Senior Analytics Engineer to architect and evolve Versaterm’s engineering metrics ecosystem. This is a highly technical, hands-on role responsible for building the data platform, integrations, and reporting layer that enables unified engineering visibility across the organization. This role goes far beyond dashboard development. You will design and operationalize the data infrastructure that ensures our engineering metrics are accurate, reliable, automated, and actionable. You will work closely with Engineering and Product leadership to define outcome‑driven KPIs and convert raw operational data into insights that guide strategic decision-making. What You’ll Do Build and evolve a scalable engineering analytics ecosystem Architect the data infrastructure, semantic layer, and source integrations required for unified engineering metrics. Implement and maintain data pipelines, transformations, APIs, and automation to ensure high-quality, consistent data. Develop standardized engineering KPIs and dashboards Develop standardized engineering KPIs and dashboards Including but not limited to: DORA metrics: deployment frequency, lead time, MTTR, change failure rate Delivery health: velocity, throughput, predictability, flow efficiency Quality: defect trends, escaped defects, test coverage signals Operational indicators: cycle time, WIP, backlog health Integrate data across engineering and product systems Jira, Confluence, Azure DevOps, GitHub, CI/CD tools Aha! (or similar roadmap tooling) Incident/support systems where applicable Build repeatable data ingestion via APIs, webhooks, and scheduled pipelines Ensure data quality and governance Implement validation frameworks and data quality checks Enforce metric definitions, naming conventions, and data modeling standards Maintain a source-of-truth metrics layer consumable by BI tools Deliver insights and partner with leadership Create executive-ready analytics, narratives, and recommendations Partner with Engineering Directors, to align on KPI definitions Identify trends, bottlenecks, and improvement opportunities to support engineering excellence Continuously improve Enhance models, pipelines, and metrics as tooling and processes mature Champion automation and reduce manual reporting across engineering What You’ll Bring Bachelor’s degree in Computer Science, Engineering, Data Engineering, or a related field 8+ years in analytics engineering, data engineering, or technical analytics roles Strong experience with SQL and data transformation tools (e.g., dbt or similar) Proven experience integrating data from systems such as Jira, Azure DevOps, GitHub, CI/CD pipelines Expertise building dashboards in BI platforms (Power BI, Tableau, Looker, or similar) Experience designing scalable, production-quality data models and semantic layers Working knowledge of APIs, Python (or similar), automation frameworks, ELT/ETL concepts Strong understanding of the software development lifecycle, DevOps metrics, and engineering processes Ability to translate complex engineering datasets into clear insights for senior leadership A practical, execution-focused mindset with a drive to create durable, repeatable solutions Note: This position requires a security clearance from the Government of Canada. Candidates must be legally authorized to work in Canada and must successfully obtain and maintain a Reliability security clearance. Please note that specific customer contracts may impose additional security verification requirements. Obtaining and maintaining all required security clearances is a condition of employment. For more information on the Government of Canada's security screening process, please visit Public Services and Procurement Canada. Versaterm is committed to fair and equitable compensation that is competitive, consistent, and aligned with the value each role contributes to our organization. The starting salary for this position will be dependent upon many factors, such as the successful candidate’s skills, experience, education, training as well as internal equity, market data, and business needs. In addition to base salary and our comprehensive benefits offering, some roles may also be eligible for variable compensation such as incentive plans or discretionary performance bonuses. Versaterm does not use AI in the recruitment process. All stages of recruitment decisions are lead by people, including our qualified acquisition team and our experienced hiring managers. Versaterm is an equal opportunity employer and is committed to equity, diversity, inclusion, and a barrier-free workplace. Accommodations are available upon request throughout all stages of the recruitment process and apply to the terms and conditions of employment. For more information, please contact info@versaterm.com.

Responsibilities

The Senior Analytics Engineer will be responsible for architecting and evolving the engineering metrics ecosystem by building the data platform, integrations, and reporting layer for unified engineering visibility. This involves designing and operationalizing data infrastructure to ensure metrics are accurate, reliable, automated, and actionable, while partnering with leadership to define outcome-driven KPIs.