Data Engineer

Responsibilities

• Design and implement dimensional and relational data models (star/snowflake schemas) that support scalable, performant analytics across business domains.
• Develop, document, and maintain transformation logic that converts raw source data into clean, conformed, and business-ready datasets.
• Write and optimize complex SQL queries and dbt models for data manipulation, aggregation, and layered transformation within the data warehouse.
• Define and enforce data standards, naming conventions, and modeling best practices to ensure consistency across the warehouse.
• Collaborate with stakeholders to translate business logic into well-structured data models that are intuitive and reusable.
• Evaluate and refine existing models over time as business needs evolve, ensuring models remain accurate, efficient, and well-documented.
• Design, develop, and maintain Python scripts for extracting and loading data from various source systems into our data warehouse.
• Ensure data quality, accuracy, and consistency throughout the pipeline, from ingestion through to reporting-ready layers.
• Identify and resolve data pipeline issues, ensuring timely and reliable data availability.
• Contribute to the documentation of ETL processes, pipeline architecture, and data definitions.
• Develop, maintain, and optimize interactive dashboards and reports using Power BI to visualize KPIs and business trends, built on top of well-modeled data layers.
• Perform ad-hoc data analysis using SQL and collaborate with stakeholders across Marketing, Sales, Product, and Operations to translate business questions into clear, actionable insights.

• Bachelor's degree in Computer Science, Mathematics, Statistics, or a related quantitative field.
• 2–4 years of experience in a data-focused role, with a strong emphasis on data engineering and/or analytics.
• Demonstrated experience designing dimensional models (star/snowflake schemas) and translating business requirements into logical and physical data models.
• Advanced SQL proficiency within a modeling and transformation context (e.g., layered CTEs, reusable views, incremental logic).
• Hands-on experience building pipelines that clean, standardize, and reshape raw data into analytics-ready datasets using SQL and/or Python.
• Practical experience with dbt for modular, tested, and version-controlled transformation logic.
• Experience developing and publishing interactive dashboards in Power BI or comparable tools such as Tableau or Looker.
• Familiarity with workflow orchestration tools such as Apache Airflow.
• Experience with cloud-based data platforms, particularly Google Big Query or Azure Synapse Analytics.
• Proficiency with Power BI report development and DAX formula creation.
• Industry experience in e-Commerce, particularly with Shopify.
• Solid experience with Python for data manipulation, scripting, and building ETL/ELT processes (e.g., Pandas, SQL Alchemy).
• Strong analytical, problem-solving, and critical thinking abilities with keen attention to detail.
• Excellent verbal and written communication skills, with the ability to explain complex data concepts to non-technical stakeholders.