Analytics Engineer

Ciklum is looking for an Analytics Engineer to join our team full-time in Canada.

We are a custom product engineering company that supports both multinational organizations and scaling startups to solve their most complex business challenges. With a global team of over 4,000 highly skilled developers, consultants, analysts and product owners, we engineer technology that redefines industries and shapes the way people live.

As an Analytics Engineer, you will be at the forefront of transforming raw data into reliable enterprise data assets that enable strategic decision‑making across the organization. Yourfocus will be building and productionalizing new Subscriptions data sets from the ground up, ensuring they support universal analytics needs for critical aspects of the business. You will collaborate closely with stakeholders to understand business needs, translate domain expertise into actionable data logic, and help shape the future of data-driven insights s role requires deep technical expertise in analytics engineering, a keen understanding of stakeholder needs, and the ability to drive alignment on data sets that serve as the single source of truth.

You will support the creation and initial operationalization of data pipelines, while ensuring smooth transitions to long‑term ownership by data engineering teams.

• Clarity & Trust:
  You have built data sets that are trusted across the organization, enabling consistent, high-quality insights
• Efficiency Gains:
  Your work has reduced time to insights, enabling faster decision‑making and saving analytics resources
• Scalability & Extensibility:
  The data assets you create are scalable and flexible, ready to adapt to future needs without major rework
• Cross-functional Alignment:
  You've driven alignment between business, data, and engineering teams, ensuring that data logic is both widely accepted and operationally feasible

• Build Enterprise Data Assets (0 →
  1):
  Lead efforts to create new data sets from scratch, focusing on foundational analytics assets that serve universal business purposes
• Enable Robust & Extensible Analytics:
  Establish widely accepted logic for critical data sets, driving alignment on a single source of truth that reduces confusion, rework, and analytical overhead
• Set Analytics Requirements:
  Collaborate with business stakeholders to capture data requirements, ensuring that new data assets are both fit for purpose and future‑proof
• Translate Domain Expertise into Data Logic:
  Work with domain experts to convert their knowledge into computational logic that underpins new data sets
• Productionalize Data Sets:
  Build and deploy v1 data sets in a way that allows the business to benefit immediately from their insights, while ensuring scalability and maintainability
• Alerting & Monitoring:
  Implement alerting mechanisms to ensure that data sets are monitored effectively, with issues flagged to appropriate teams for timely resolution
• External Reporting Support:
  Enable data exports to external parties by supporting development and testing of reporting data sets
• Change Management Support:
  Help stakeholders manage changes to business logic and analytics requirements, especially when upstream data sets evolve (e.g., claim rewrite, coupon migration)

• Bachelor'sor Master'sdegreein
  
  Computer
  
  Science,Data Science,Analytics,ora related field
• 4+yearsofexperienceindataengineering,analyticsengineering,orrelatedfields,with a proven track record of building and maintaining large-scale data assets
• ExpertiseinSQLforqueryinganddatatransformation
• Strongprogrammingskillsin Pythonfordatamanipulation,automation,and building data pipelines. Experience with frameworks like Pandas,Num Py, andPySpark is preferred
• Experiencewithclouddataplatformssuchas Snowflake,Big Query,orAWS Redshift, including working with cloud-native tools for data integration and transformation
• ExperiencewithETLorchestrationtoolssuchas
  
  Airflowformanagingandscheduling DAGs, ensuring that workflows are efficient, reliable, and scalable
• Familiaritywithdatamodelingconceptssuchasstar/snowflakeschemasandbuilding logical and physical data models for analytics use…