Description:
As an Analytics Engineer, you'll be at the heart of the company’s data evolution, developing a robust data warehouse to drive insights and enable smarter decision-making. You’ll work hands-on with tools like dbt, Snowflake, and Airflow to build scalable data models and optimize pipelines, owning the "T" in ETL. If you’re a problem-solver who thrives on autonomy and enjoys crafting clean, efficient data solutions, this role is perfect for you.
Key Responsibilities:
- Data Modeling & Transformation: Create structured data models and transform raw data into accessible insights using dbt, SQL, and Snowflake.
- Project Execution: Help build a modernized data warehouse focused on core metrics (sales, inventory, product etc.) and support future data expansion and feature development.
- Data Pipeline Optimization: Design and manage data transformation workflows, leveraging Airflow to streamline data flow from ingestion to analytics.
- Cross-Functional Collaboration: Work with data engineers, Analysts, and key business stakeholders to deliver impactful data solutions aligned with business needs.
- SQL Optimization: Develop and optimize SQL queries, ensuring performance efficiency with techniques like partitioning and clustering.
What You’ll Need to Succeed:
- Experience: 3-5 years in Data or Analytics Engineering, with hands-on experience in data modeling and building scalable data warehouses.
- Technical Expertise: Proficient in SQL and schema design (e.g., Snowflake, Data Vault); experienced with dbt and orchestration tools like Airflow.
- Data Transformation Mastery: Familiarity with ETL best practices and data governance tools.
- Collaborative & Independent: Strong team player with the ability to work independently, drive projects, and solve complex problems with minimal supervision.