Data Engineer Intermediate

Job Summary

We are seeking a skilled Data Engineer to design, build, and manage scalable ETL pipelines supporting a centralized data lake and Snowflake data warehouse. The role focuses on automating data ingestion, transformation, and aggregation workflows to enable reliable analytics and data-driven decision-making.

Key Responsibilities

• Design, develop, and maintain robust ETL pipelines for ingesting data into the enterprise data lake and Snowflake environment.
• Automate data processing, aggregation, and analytical workflows to improve data availability and performance.
• Implement and manage orchestration and scheduling of data pipelines using Control$B!>(BM and Apache Airflow.
• Develop scalable data transformation logic using PySpark and Apache Spark (Java).
• Work with large, structured and semi-structured datasets on AWS infrastructure.
• Ensure data quality, integrity, and reliability across data pipelines.
• Optimize data pipelines for performance, cost, and scalability.
• Collaborate with analytics, data science, and business teams to understand data requirements.
• Monitor, troubleshoot, and resolve pipeline failures and performance bottlenecks.
• Follow best practices for data engineering, security, and documentation.

Required Skills & Qualifications

• Strong experience with data lake architectures and large-scale data processing.
• Hands-on experience with AWS services (e.g., S3, EC2, EMR, Glue, or related).
• Proven expertise in building ETL pipelines for analytics and reporting use cases.
• Solid working knowledge of Snowflake, including data loading, transformations, and performance optimization.
• Experience with workflow automation and scheduling tools such as Control$B!>(BM and Apache Airflow.
• Proficiency in PySpark for distributed data processing.
• Strong programming experience with Apache Spark using Java.
• Good understanding of data modeling, partitioning, and performance tuning concepts.