Data Engineer ETL

We are seeking a highly motivated Data Engineer (ETL) to design, develop, and maintain scalable data engineering solutions supporting enterprise data aggregation and integration initiatives. This role focuses on building high-performance ETL pipelines, developing Apache Spark applications, processing large-scale datasets, and leveraging cloud-native technologies to deliver reliable and efficient data solutions. The ideal candidate will have strong expertise in Spark, Scala, Java, cloud platforms, and big data technologies while collaborating with cross-functional teams in an Agile environment.

• Design, develop, and maintain scalable ETL pipelines for enterprise data integration and movement.
• Develop, test, deploy, and support ETL batch jobs using Apache Spark and Amazon EMR.
• Build and maintain Spark services for large-scale data processing.
• Schedule, monitor, troubleshoot, and optimize Spark batch processing jobs.
• Develop Java clients for consuming REST and SOAP APIs.
• Develop and maintain Scala-based Spark batch applications.
• Process and transform large datasets using Apache Spark.
• Store and manage processed data in No
  
  SQL databases such as Cassandra or Yugabyte.
• Design and implement cloud-based data warehouse and data lake solutions using Snowflake.
• Work with large-scale data storage formats including Parquet and HDF
  5.
• Collaborate with cross-functional teams to design innovative data solutions that address business requirements.
• Participate in code reviews, pair programming, and technical discussions to ensure code quality and best practices.
• Support the full Software Development Life Cycle (SDLC), including analysis, design, development, testing, deployment, and maintenance.
• Troubleshoot production issues and optimize data processing performance.
• Identify opportunities for innovation and continuous improvement in data engineering practices.
• Collaborate across multiple Agile teams to deliver scalable and reliable data solutions.
• Apply a customer-first mindset when designing and delivering enterprise data solutions.

• Bachelor's degree in Computer Science, Information Systems, or a related field.
• 10+ years of experience developing Spark services for large-scale data movement.
• Experience scheduling, monitoring, debugging, and optimizing ETL Spark batch processes.
• Hands-on experience developing Java clients for REST and SOAP API integrations.
• Experience developing Scala Spark batch applications.
• Experience developing, deploying, and maintaining Apache Spark and Amazon EMR ETL jobs.
• Experience processing big data using Apache Spark.
• Experience with No
  
  SQL databases such as Cassandra or Yugabyte.
• Experience with cloud-based data warehouse and data lake platforms such as Snowflake.
• Experience managing large datasets using storage formats such as Parquet and HDF
  5.
• Strong understanding of software development lifecycle (SDLC) methodologies.
• Strong analytical, troubleshooting, and problem-solving skills.
• Experience working in Agile software development environments.
• Excellent collaboration and communication skills.

• Experience designing enterprise-scale data integration and aggregation solutions.
• Experience supporting external-facing data platforms and APIs.
• Experience working with financial services or Fin Tech data platforms.
• Passion for continuous learning and adopting modern data engineering best practices.