We are looking for a Mid Data Engineer who will focus on designing, building, and maintaining scalable ETL/ELT data pipelines using Databricks and AWS, enabling reliable data ingestion, transformation, validation, and delivery for downstream analytics and reporting. You will work closely with the Data Manager and junior Data Engineers, independently developing pipelines from scratch while ensuring data quality, performance, and production readiness across data lake/data warehouse environments.

Responsibilities

Design, develop, and maintain ETL/ELT data pipelines using Databricks and AWS.
Build data pipelines from scratch across ingestion, transformation, validation, and delivery.
Integrate data from databases, APIs, cloud storage, and other source systems.
Develop data processing solutions using SQL, Python, and Databricks.
Monitor data quality, troubleshoot pipeline issues, and optimize performance.
Support data lake/data warehouse environments and prepare datasets for Power BI reporting.
Collaborate with the Data Manager and junior Data Engineers on data solutions and technical requirements.
Document pipelines and participate in testing and production deployment.

Required Qualifications

3–5+ years of experience in Data Engineering or ETL Development.
Hands-on experience with Databricks and AWS for data engineering.
Proven experience building data pipelines from scratch.
Strong SQL skills.
Strong Python skills.
Experience with ETL/ELT, data integration, and data transformation.
Knowledge of data lake/data warehouse concepts and data quality practices.

Preferred Qualifications

Experience with PySpark, Delta Lake, and Databricks Workflows.
Experience with AWS services such as S3, Glue, Lambda, or Redshift.
Experience with Git/GitHub and Power BI.
Retail, e-commerce, or health & wellness industry experience.

Databricks & AWS Data Engineer