SENIOR DATA ANALYTICS ENGINEER – CUSTOMER EXPERIENCE & PRODUCT ANALYTICS:

On behalf of our Accounting client, Procom is searching for a Senior Data Analytics Engineer – Customer Experience & Product Analytics for a 14 month contract role. This position is a hybrid position with 2 days onsite at our client’s Toronto, Ontario office.

SENIOR DATA ANALYTICS ENGINEER – CUSTOMER EXPERIENCE & PRODUCT ANALYTICS - JOB DESCRIPTION:

Our large Financial/Accounting SaaS Client is seeking a Senior Data Analytics Engineer to drive customer experience and product analytics. This is a high-visibility, business-critical role that directly impacts how millions of users engage with the Accounting software product.

SENIOR DATA ANALYTICS ENGINEER – CUSTOMER EXPERIENCE & PRODUCT ANALYTICS - MANDATORY SKILLS:

• 5+ years of hands-on experience in Data Analytics, Business Intelligence, or Analytics Engineering.
• Proficiency in SQL, Tableau (required), and Python scripting.
• Cloud experience (AWS required; GCP a plus).
• Demonstrated expertise applying Propensity Score Matching (PSM) in marketing or customer analytics.
• Experience with data pipeline management, schema design, and large dataset transformations.
• Strong communication skills with a proven ability to influence non-technical stakeholders.
• Strategic thinker with customer empathy and passion for improving user experience.

SENIOR DATA ANALYTICS ENGINEER – CUSTOMER EXPERIENCE & PRODUCT ANALYTICS – NICE-TO-HAVE SKILLS:

• Experience with Qlik Sense, QuickETL, or R.
• Exposure to enterprise-scale data migration and governance.

SENIOR DATA ANALYTICS ENGINEER – CUSTOMER EXPERIENCE & PRODUCT ANALYTICS – ASSIGNMENT LENGTH:

This is a 14 month contract position.

SENIOR DATA ANALYTICS ENGINEER – CUSTOMER EXPERIENCE & PRODUCT ANALYTICS - ASSIGNMENT LOCATION:

Toronto, Ontario, Canada. This position is hybrid with 2 days onsite

• Analyze customer journeys and identify friction/drop-off points across the accounting platform’s digital experience.
• Translate data into insights that directly influence marketing campaigns, product enhancements, and financial strategies.
• Partner with data scientists, product managers, and marketers to guide customer engagement initiatives.
• Regularly present findings to senior stakeholders, building a clear narrative through dashboards, reports, and visualizations.
• Pull, clean, and analyze large datasets from Tableau and SQL; build scalable data pipelines and ensure high data quality.
• Apply advanced statistical methods (including Propensity Score Matching) to optimize customer segmentation and targeting strategies.
• Leverage cloud platforms (AWS/GCP), Databricks, and dbt for scalable analytics engineering.
• Drive data literacy across the organization by promoting self-service BI and sustainable practices.