Senior Data Systems Analyst to lead end-to-end data systems analysis using SQL, ETL, and data warehouse/lake environments - 2893

Our financial services client (insurance sector) is seeking a Senior Data Systems Analyst (7+ years) to lead end-to-end data systems analysis using SQL, ETL, and data warehouse/lake environments

Duration: Until 30 Sep (high possibility of extension)

Location: Markham - Hybrid - 3 days a week required, subject to change

We are seeking a collaborative and resourceful Data Systems Analyst for our fast-paced Technology Delivery Data function. You will be leading the data systems analysis team and help scale the data and analytics capabilities. You will be responsible for being a liaison between various business units and technical data teams. You'll be required to apply your depth of knowledge and expertise to all areas including requirements, documentation, road mapping, data analysis, and data analytics. Further, you will be part of the data transformation initiatives to help build the right data mindset to enable our direct-to-consumer business, data science capabilities, and expand our digital footprint.

This role will be part of and a member of our Information Technology Data Delivery Group. You will be responsible for creating the center of excellence for the data ecosystem artifacts creation, maintenance, and project requirements in a high-performing, and experienced team. You'll be required to apply your depth of knowledge and expertise to all many areas including requirements, infrastructure, and solution documentations.

Must Haves

• Minimum 5 years’ of experience successfully leading Data Systems Analysis organizations with expertise in building large-scale enterprise data assets.
• 8+ years’ experience as a Business Analyst working on mid-large projects for data design, development, and implementation of business-critical enterprise data systems.
• Strong data analysis background with advanced SQL expertise
• Extensive experience in ETL and data engineering across enterprise environments
• Proven background in data warehouse and data lake ecosystems
• Solid grasp/experience with data technologies & tools (Snowflake, Hadoop, PostgreSQL, Informatica, etc.,)
• Outstanding knowledge and experience in ETL with Informatica product suite.
• Experience establishing documentation standards frameworks for data quality, data governance, stewardship and metadata management.
• Ability to foundationally understand complex business process driving technical systems.
• Strong leadership and influencing skills at the senior management level.
• Strong analytical, critical thinking and problem-solving skills.
• Strong stakeholder management.
• Solid understanding of Project and Program Management processes.
• Excellent verbal and written communication skills.
• Insurance knowledge an asset.

Nice to Have

• Knowledge of insurance industry data, claims, and policy processes

Responsibilities

1. Claims & Policy Data Analysis

• Analyze structured and semi-structured data related to claims, policies, underwriting, and customer interactions.
• Identify patterns in claims frequency, fraud indicators, and loss ratios using lakehouse datasets.
• Support actuarial teams with data extracts and trend analysis.

2. Customer & Risk Insights

• Segment customers based on behavior, risk profiles, and product usage.
• Analyze customer lifetime value, churn risk, and cross-sell/up-sell opportunities.
• Collaborate with risk and compliance teams to monitor exposure and regulatory thresholds.

3. Regulatory & Compliance Reporting

• Prepare data extracts and reports for regulatory bodies (e.g., OSFI, FSRA, NAIC).
• Ensure data lineage and traceability for audit and compliance purposes.
• Validate data accuracy and completeness for filings and disclosures.

4. Data Wrangling & Preparation

• Clean and transform raw data from diverse sources (e.g., policy admin systems, CRM, claims systems) into analytics-ready formats.
• Leverage lakehouse tools (e.g., Delta Lake, Apache Iceberg) to manage versioned and time-travel datasets.
• Collaborate with data engineers to ensure efficient ETL/ELT processes.

5. Business Intelligence & Visualization

• Build dashboards and visualizations for underwriting, claims, finance, and product teams.
• Use tools like Power BI, Tableau, or Qlik to present insights from lakehouse data.
• Enable self-service analytics by creating reusable datasets and semantic layers.

6. Data Quality & Governance

• Profile and validate data to ensure consistency across policy, claims, and financial domains.
• Tag and catalog datasets using metadata tools (e.g., Unity Catalog, Collibra).
• Support master data management and reference data initiatives.

7. Collaboration & Stakeholder Engagement

• Work with actuaries, underwriters, product managers, and IT teams to understand data needs.
• Translate business questions into analytical queries and data models.
• Document business logic, assumptions, and data definitions clearly.

8. Predictive & Advanced Analytics Support

• Assist data scientists with feature engineering and exploratory data analysis.
• Provide historical data extracts for model training and validation.
• Interpret model outputs and integrate them into business reporting.