The Data Solutions Engineer is an intermediate-level position focused on building next-generation Data Engineering solutions. This role involves collaborating with the Technology team and acting as a liaison between business users and technologists. The primary focus is on designing and developing Big Data solutions, migrating legacy workloads to cloud platforms, and ensuring robust data pipelines. The engineer will also mentor team members and drive the implementation of best practices in data engineering.

• 5+ years of experience with Hadoop and Big Data technologies
• Demonstrated proficiency in Python, PySpark, and Scala, including practical experience with fundamental machine learning libraries
• Experience in developing robust data solutions leveraging Google Cloud or AWS platforms; relevant certifications are preferred
• Experience with SAS
• Experience with containerization and related technologies (e.g., Docker, Kubernetes)
• Comprehensive understanding of software engineering and data analytics
• In-depth knowledge and hands-on experience with the Hadoop ecosystem and Big Data technologies (e.g., HDFS, MapReduce, Hive, Pig, Impala, Kafka, Kudu, Solr)
• Knowledge of Agile (Scrum) development methodologies
• Strong development and automation skills
• System-level understanding of data structures, algorithms, distributed storage, and compute

• Serve as an integral team member of the Data Engineering team, responsible for the design and development of Big Data solutions
• Partner with domain experts, product managers, analysts, and data scientists to develop robust Big Data pipelines in Hadoop or Snowflake environments
• Responsible for delivering a data-as-a-service framework
• Lead the migration of all legacy workloads to cloud platforms
• Engage with key stakeholders to elicit and document requirements, including detailed data flow specifications
• Assess appropriate solutions and collaborate with relevant teams to drive optimal implementations
• Work with data scientists to build client pipelines using heterogeneous sources and provide essential engineering services for data science applications
• Research and evaluate open-source technologies and components, recommending and integrating them into design and implementation efforts
• Act as a technical expert, mentoring other team members on Big Data and Cloud technology stacks
• Define comprehensive requirements for maintainability, testability, performance, security, quality, and usability across the data platform
• Drive the implementation of consistent patterns, reusable components, and coding standards for all data engineering processes
• Convert SAS-based pipelines into modern languages like PySpark and Scala for execution on Hadoop and non-Hadoop ecosystems
• Optimize Big Data applications on both Hadoop and non-Hadoop platforms for peak performance
• Evaluate new IT developments and evolving business requirements, recommending appropriate system alternatives and/or enhancements to current systems through analysis of business processes, systems, and industry standards
• Appropriately assess risk when making business decisions, demonstrating consideration for the firm's reputation and safeguarding Citigroup, its clients, and assets

• Citi offers a comprehensive benefits package including competitive pay, stock programs, healthcare coverage, retirement plans, paid time off, parental leave, and programs supporting employee wellbeing and professional development.