Data Engineer - Manhattan West, NY / Naveen

Dice is the leading career destination for tech experts at every stage of their careers. Our client, Ampcus Inc, is seeking the following. Apply via Dice today! Title: Data Engineer Intermediate Position Type: Contract to hire Location: Manhattan West, NY- Onsite Onsite 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 ControlM 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 ControlM 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.