Data Engineer

Job Description

Role Overview

This role requires deep technical expertise and a passion for building resilient ETL/ELT data pipelines using PySpark within an AWS cloud environment.

• Design, develop, and maintain scalable ETL/ELT pipelines with PySpark in AWS.
• Automate data ingestion and processing workflows using CI/CD frameworks.
• Curate, cleanse, and transform large datasets for business and analytical reporting.
• Ensure the availability and integrity of data for Power BI dashboards and reports.
• Implement best practices in data lake architecture, data warehousing, and schema design (star/snowflake model).
• Document processes, troubleshoot data flow issues, and optimize pipeline performance.

• Hands-on expertise in ETL/ELT pipeline development using PySpark within the AWS ecosystem (e.g., S3, EMR, Glue).
• Advanced programming skills in Python, with deep knowledge of PySpark.
• Proven experience with building and managing data lakes and data warehouses.
• Strong understanding of star and snowflake schema designs for analytics workloads.
• Experience automating deployments and workflows using CI/CD pipelines (e.g., AWS CodePipeline, Jenkins, GitHub Actions).
• Expertise in preparing curated datasets for BI and reporting tools, especially Power BI.

• Experience collaborating with Business Analysts, Data Scientists, and ERP functional teams to interpret business requirements.
• Strong analytical skills and creative problem-solving abilities.
• AWS Certified Data Analytics – Specialty, Databricks/Spark Developer Certification, and/or Microsoft Power BI Data Analyst Associate certification.
• Familiarity with data governance and security best practices in cloud data environments.

• Competitive compensation and comprehensive benefits package.
• Opportunities for professional development and industry certification support.
• Work alongside a dynamic, innovative, and collaborative data and analytics team.
• Exposure to cutting-edge cloud and data technologies.