Senior Analytics Engineer – Capital Markets Data Modelling Contract Duration: 1 year Work Arrangement: Hybrid – 1 day per week in office Rate: $80–$90/hour, incorporated Ideal Candidate Profile The ideal candidate is a senior-level data professional who combines strong data modelling expertise, hands-on Databricks development, and business-facing solution ownership. Candidates should be experienced in designing analytical data structures and transforming complex business requirements into trusted, reusable data products. This position is best suited to someone who enjoys working at the intersection of business analysis, data modelling, and analytics engineering, rather than a candidate focused primarily on infrastructure, platform administration, or pipeline orchestration. Role Overview We are seeking a Senior Analytics Engineer to lead data modelling and solution design for business-critical Capital Markets data domains. This role combines advanced data modelling, analytics engineering, and hands-on development to deliver reliable, reusable data assets that support business decision-making and analytical capabilities. Working closely with business stakeholders and data engineering teams, the successful candidate will translate business requirements into scalable data models and curated data products using Databricks. The primary focus is data architecture at the modelling and business solution level, rather than platform engineering or pipeline infrastructure. The ideal candidate brings strong SQL expertise, practical Databricks experience, and the ability to take ownership of data solutions from requirements gathering through production delivery.

Key Responsibilities
Business Engagement & Solution Design
• Serve as the primary data modelling and design resource for an assigned Capital Markets business domain.
• Collaborate with business stakeholders to understand reporting needs, analytical requirements, and operational challenges.




• Translate business concepts and requirements into well-structured, scalable data solutions.
• Lead solution design from initial discovery and requirements gathering through development, validation, and production deployment.
• Establish reusable design patterns that support maintainability, performance, and cost efficiency. Data Modelling & Transformation
• Design and maintain conceptual, logical, and physical data models to support business and analytical use cases.
• Develop dimensional models, including star schemas, historical data structures, and slowly changing dimensions (SCDs).
• Implement data models across the appropriate layers of a medallion architecture.
• Build SQL and Python/PySpark transformations to produce consistent, validated, business-ready datasets.
• Define data standards, business terminology, metadata, and documentation for domain-specific data products.
• Incorporate data quality checks, reconciliation, and validation into modelling and transformation processes. Data Products & Analytics Enablement
• Develop curated datasets and semantic models that support reporting, self-service analytics, and machine learning initiatives.
• Work with business users to investigate complex data questions and resolve inconsistencies.
• Convert recurring ad hoc data requests into sustainable, reusable data solutions.
• Support the development of trusted data products that enable consistent business metrics and reporting.




• Share modelling expertise and contribute to improved data development practices across the wider team. Required Skills & Experience
• Degree in Computer Science, Engineering, Mathematics, Finance, or a related discipline.
• 6+ years of experience in data engineering, analytics engineering, data modelling, or business intelligence, with a strong emphasis on data modelling.
• 2+ years of hands-on Databricks experience, including Delta Lake, Unity Catalog, and Databricks SQL.
• Advanced SQL skills and strong knowledge of dimensional data modelling, including star schemas and slowly changing dimensions.
• Proficiency in Python and PySpark for data transformation and modelling.
• Demonstrated experience engaging directly with business stakeholders to gather requirements and translate them into technical solutions.
• Experience designing and delivering data solutions across development, testing, and production environments.
• Strong understanding of data quality, validation, documentation, and data governance principles.
• Ability to explain complex data concepts clearly to both technical and non-technical audiences.
• Strong analytical, problem-solving, and communication skills. Nice-to-Have Qualifications
• Experience with reporting and visualization tools such as Power BI, Plotly, or Databricks AI/BI Dashboards.
• Familiarity with semantic modelling and AI-enabled business intelligence capabilities, including Unity Catalog Metric Views and Databricks Genie.
• Experience supporting large-scale data migration initiatives.
• Databricks Certified Data Engineer – Associate or Professional.
• Experience working with enterprise-scale analytical data products and contemporary cloud-based data platforms. Note: We use AI tools to: obtain basic information, detect plagiarism, false employment history or references, categorize your skills, and do an initial match with job posting.

📌 Data Analytics Engineer (Toronto)
🏢 Infotek Consulting
📍 Toronto