Engineer Jobs

About The Position

Requirements

• 10 or more years of experience in Data Engineering; the description also cites typically 10 or more years of data delivery or integration experience.
• Hands-on experience with Scala, Spark, and Kafka Streaming.
• Very good understanding of Hadoop components, architecture, and toolsets, including Spark and Scala, Informatica, Starburst, and object storage in enterprise data platforms.
• Experience architecting, developing, and implementing solutions using real-time streaming technologies.
• Knowledge of performance tuning.
• Experience with Exadata and Oracle SQL tuning.
• Experience with Starburst and object storage.
• Experience developing streaming, near-real-time, and batch data integration solutions.
• Hands-on experience with UNIX shell scripting.
• Working experience with scheduling tools.
• Knowledge of Hadoop Spark/Spark Streaming best practices and standards, as well as standard software development lifecycle experience.
• Ability to quickly grasp technology concepts.
• Solid quantitative, analytical, process development, facilitation, and organizational skills.
• Strong communication, presentation, interpersonal, software development, and work management skills.
• Experience developing software that meets functional, non-functional, and compliance requirements and integrates into wider applications or systems through robust, well-tested interfaces.
• Ability to lead multiple projects with competing deadlines.
• Experience with Agile processes.

Nice To Haves

• Experience with large-scale analytical platforms, metadata-driven architectures, and distributed data processing frameworks.
• CICD build and deployment tools.
• API development experience.
• Financial Services or Banking experience.
• Experience in distributed computing.
• Experience with open table formats such as Apache Iceberg, Delta Lake, or equivalent technologies.
• Knowledge of Treasury Payments and Deposits domains.

Responsibilities

• Assemble large, complex data sets that meet functional and non-functional requirements, maintaining consistency across systems in design and engineering approaches.
• Maintain, improve, clean, and manipulate large data sets for operational and analytical systems; build processes for data transformation, data structures, metadata, data quality controls, dependencies, and workload management.
• Communicate information needed for deployment, maintenance, and support of business functionality.
• Use multiple architectural components to design and develop client requirements, collaborating with development teams to ensure the data architecture is feasible to implement.
• Define and build data pipelines that support data-informed decision-making while adhering to release processes and risk management routines.
• Contribute to integration, regression, and performance test suites; analyze test reports, identify issues and errors, and lead triage of underlying causes.
• Identify gaps in adherence to data management standards and work with partners to develop plans to close them.
• Lead concept testing and research to prototype toolsets and improve existing processes.
• Mentor Data Engineers in continuous integration and continuous delivery events, and define key performance indicators and internal controls.
• Lead requirements analysis, design, development, and implementation of enterprise-scale data platforms and integration solutions.
• Conduct design reviews, mentor and coach development teams, and produce flexible solution designs aligned with the larger portfolio.
• Coordinate with deployment and support teams to ensure projects are deployed successfully into testing and production environments.
• Lead multiple projects concurrently and coordinate work with teams in different time zones.
• Design, develop, and deploy real-time, near-real-time, and batch data integration solutions.

Benefits

• Affordable, competitive, and flexible benefits supporting teammates’ physical, emotional, and financial wellness.