Analytics Engineer
Requirements
- Advanced SQL proficiency – query optimization, window functions, complex joins, performance tuning on large datasets.
- Hands-on experience with the AWS data ecosystem – Redshift/Athena (Spark/Glue will be a plus).
- Python scripting for data processing/automation.
- Experience with Apache Airflow for pipeline orchestration.
- Experience building and maintaining dashboards/data sources in Tableau.
- Ability to communicate data logic and insights clearly to both technical and non-technical stakeholders.
- Solid understanding of data warehousing concepts – dimensional modeling, star/snowflake schemas, slowly changing dimensions.
Would be a plus
- Familiarity with the concept of Data Lakehouse and open table formats (Apache Iceberg)
- Experience with dbt for transformation and modeling.
- Experience with marketing/attribution data (Meta, Google Ads, TikTok, AppsFlyer) or mobile app analytics.
- Familiarity with CI/CD practices for data pipelines.
Responsibilities
- Design, build, and maintain ETL/ELT pipelines using SQL/Apache Spark (AWS Glue) and Airflow.
- Model and optimize data in Amazon Redshift and query large datasets via Amazon Athena.
- Write advanced SQL to transform raw data into clean, joinable, analysis-ready tables.
- Build and maintain Tableau dashboards and data sources, ensuring accuracy, performance, and usability for business stakeholders.
- Partner with stakeholders (marketing, product, BI) to translate business questions into robust data models.
- Ensure data quality by implementing checks/monitoring and documenting the data warehouse.
- Improve pipeline reliability, performance, and cost-efficiency on AWS, including evaluating and adopting dbt for transformations.
