Analytics Engineer Sr

Overview

Analytics Engineer (Senior) supporting a client by delivering reliable data pipelines, reporting, and actionable insights, including cloud-based ETL/ELT workflows and Tableau dashboards.

What you'll do

• Lead the development and maintenance of reusable, analysis-ready datasets for reporting, dashboarding, and analytical use cases.
• Translate business questions into structured analytical approaches, reporting requirements, data needs, and output designs with limited oversight.
• Analyze structured data to identify trends, anomalies, business drivers, and actionable opportunities.
• Own lightweight transformation logic and dashboard-ready data modeling needed to support accurate and scalable analytics outputs.
• Design and maintain ingest and business logic-based transformations in tools such as Alteryx, Databricks SQL, Pyspark and related cloud data environments.
• Build scalable ingestion pipelines using APIs and other connectors to ingest, transform and centralize data in analysis, data science and dashboard ready data sets.
• Build scalable analytical models that support Data Analysts, Data Scientists, AI Scientists, and business stakeholders with reliable and understandable data assets.
• Perform and improve data validation, QA, and logic review practices to ensure modeled datasets are accurate, complete, and aligned with business expectations.
• Troubleshoot moderately complex modeling, transformation, semantic, or metric consistency issues and work with adjacent teams to resolve them.
• Document transformation logic, source dependencies, joins, model assumptions, semantic definitions, and data quality considerations clearly and consistently.
• Support structured code review, version-controlled development, and reusable workflow practices for analytics engineering assets.
• Partner with Data Analysts, Data Scientists, AI Scientists, Data Engineers, and Architects to ensure transformed datasets align with reporting needs, modeling requirements, governed data structures, and platform realities.
• Improve delivery quality by identifying opportunities for better model structure, stronger metric consistency, cleaner documentation, and more scalable analytical workflows.
• Follow established governance, privacy, and data-quality standards in day-to-day work.
• Create dashboards in Power BI, Tableau, or similar BI tools.
• Develop Alteryx and other ETL for automated reporting.
• Translate complex analytical outputs into executive-friendly visualizations.
• Build, maintain, and support interactive reports with filters, drill-downs, and trend analysis.
• Ensure data quality and consistency behind dashboards and enable business teams to understand reporting requirements and track numbers, rates, growth, correlation, etc.
• Serve as a dependable analytics engineering partner to internal teams and client stakeholders across transformation logic and reusable data asset needs.
• Work with Data Scientists and AI Scientists to ensure analytical and AI workflows are supported by reliable, governed, and appropriately modeled datasets.
• Participate confidently in client-facing discussions by explaining model logic, metric definitions, transformation choices, and data quality considerations in clear business language.
• Help improve team consistency by strengthening documentation, sharing modeling best practices, and supporting more junior practitioners.

What you'll need

• A minimum of 3 years of hands-on experience in data engineering, cloud data platforms, and enterprise analytics solutions.
• Bachelor's degree in Computer Science, Data Science, Information Systems, Engineering, Mathematics, Statistics, or a related technical field.
• Databricks (Python, PySpark, SQL, Delta Lake, Workflows).
• Cloud data platforms and services (Azure/Databricks).
• API integrations and data ingestion frameworks.
• ETL/ELT pipeline design, development, and support.
• Workflow orchestration tools such as Airflow, Databricks Workflows, or Azure Data Factory.
• Data modeling, performance optimization, monitoring, and operational support.
• Power BI or enterprise BI platform development.
• Data governance, security, access management, and production support.
• Data validation and automated data anomaly detection.

Nice to have

• Experienced in Agile methodologies and consulting.
• Familiarity with Finance KPI and spatial data.

Details

• Location: Canada, remote.
• Flexibility with remote and hybrid work options (country-dependent).