Analytics Engineer

SUMMARY

The Analytics Engineer acts as a full-stack data professional , owning the end‑to‑end flow of data—from ingestion and transformation to modeling, analysis, and insight delivery. This role blends the strengths of a Data Analyst and a Data Engineer, ensuring the business has clean, reliable, analytics-ready data and the ability to unlock insights quickly and independently.

The Analytics Engineer creates scalable data models, supports BI teams, and works directly with business stakeholders to turn raw data into decisions. (existing role)

RESPONSIBILITIES

Full-Stack Data Work (End-to-end Ownership):

• Extract, clean, transform, and validate datasets across systems
• Build robust, reusable data pipelines (ELT/ETL) with modern tools
• Create analytics-ready datasets for BI and business teams
• Support cloud data infrastructure (Azure, Snowflake)

Data Modeling & Semantic Layer Development

• Build and maintain scalable data models used by reporting and analytics teams
• Define business-friendly metrics, dimensions, and standardized logic
• Document data lineage and transformation logic for transparency and governance

Advanced Analytics & Insight Generation

• Analyze data to identify trends, diagnose performance issues, and support decision‑making
• Use Python/SQL to automate recurring analytical tasks
• Support predictive or prescriptive analytics when needed (e.g., forecasting, trend detection)
• Proactively spot data quality gaps and recommend improvements

BI Enablement & Visualization

• Build, maintain, and optimize Power BI dashboards and reports
• Translate complex data into clear visuals and business‑ready narratives
• Act as a partner to business stakeholders across Supply Chain, Operations, and Finance

Cross-Functional Collaboration

• Work with Data Engineers to ensure reliability, performance, and scalability
• Work with Analysts and business teams to define requirements and close knowledge gaps
• Communicate findings to technical and non‑technical audiences
• Participate in agile ceremonies, sprint reviews, and data governance activities

EXPERIENCE AND EDUCATION

• 3- 5 years of experience
• University/College degree in Data Science, Analytics, Software Engineering, Computer Science, Math, or related field
• Certifications in Power BI, SQL, Azure, Snowflake, or Python are an asset
• Experience in SQL for data extraction and transformation
• Familiarity with Python or R for scripting, automation, and exploratory analysis
• Experience with BI tools (Power BI, Tableau, Looker)
• Understanding of ETL/ELT concepts, cloud data platforms, and data modeling
• Strong communication skills and an analytical mindset