Software Engineer III- Data Engineering

As a Data Engineer at JPMorgan Chase within the Employee Platforms team, you will be responsible for executing creative software and data solutions, developing secure high-quality production code and data pipelines, and identifying opportunities to automate remediation of recurring issues. You will work closely with business stakeholders to understand requirements, design appropriate solutions, and implement robust monitoring and alerting systems to optimize performance and throughput. Additionally, you will design and implement scalable data frameworks for workforce data analytics and share best practices with Platform and Architecture teams. Key Responsibilities: - Execute creative software and data solutions by thinking beyond routine approaches - Develop secure, high-quality production code and data pipelines - Identify opportunities to automate remediation of recurring issues - Lead evaluation sessions with external vendors, startups, and internal teams - Work with business stakeholders to understand requirements and design appropriate solutions - Implement monitoring and alerting systems to proactively address data ingestion issues - Implement data quality checks and validation processes - Design and implement scalable data frameworks for workforce data analytics - Share best practices with Platform and Architecture teams - Gather, analyze, and synthesize large data sets to improve user experiences - Lead and contribute to software engineering communities of practice Qualifications Required: - Formal training or certification on software engineering concepts and 3+ years of applied experience - Hands-on experience in data engineering, including design, application development, testing, and operational stability - Advanced proficiency in data processing frameworks and tools such as Parquet, Iceberg, PySpark, Glue, Lambda, Databricks, and AWS data services - Proficiency in programming languages such as Python, Java, or Scala for data processing and application development - Extensive hands-on experience with Databricks, including architecting and managing large-scale data pipelines, optimizing Spark jobs, implementing security and governance controls, and automating workflows - Proficiency in automation and continuous delivery methods utilizing CI/CD pipelines with tools like Git/Bitbucket, Jenkins, or Spinnaker - Hands-on practical experience delivering system design, application development, testing, and operational stability - Demonstrated proficiency in software applications and technical processes within technical disciplines such as cloud, artificial intelligence, machine learning, and mobile - Experience with scheduling tools like Autosys or Airflow for job scheduling and workflow management Note: Preferred qualifications include AWS and Databricks certifications, proficiency in relational databases, and skilled in writing SQL queries.,