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): Own the full data lifecycle from raw ingestion to final insight:

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