Data Solutions Engineer

• Build and maintain scalable, high-performance data pipelines that support analytics, reporting, and machine learning capabilities across the organization.

• Develop Big Data solutions using Apache Spark, Hadoop, and cloud-native services to process and manage large volumes of data reliably.

• Partners with data scientists and analytics teams to engineer data pipelines that support advanced analytics and machine learning use cases.

• Collaborate with architects and senior engineers to gather requirements and contribute to the implementation of data platform designs.

• Apply data quality controls, monitoring, and automation to maintain data governance and auditability standards.

• Research emerging technologies and share recommendations that could improve platform performance, scalability, or maintainability.

• Support the development of junior engineers by sharing knowledge and technical best practices within the team.

• Participate in Agile delivery activities including sprint planning, development, testing, and production support.

• 5+ years of experience in data engineering, software engineering, or a closely related discipline.

• Practical experience designing and building large-scale data pipelines and distributed data processing solutions in production environments.

• Hands-on development experience using Python and Apache Spark (PySpark) to process and transform data at scale.

• Experience working with cloud-based data platforms, with exposure to AWS or Google Cloud Platform (Google Cloud Platform).

• Working knowledge of data warehousing concepts, data modeling, and distributed processing frameworks including Spark, Hadoop, Kafka, or Hive.

• Drive implementation, consistent patterns, reusable components, and coding standards for data engineering processes

• Tune Big data applications on Hadoop and non-Hadoop platforms for optimal performance

• Be the technical expert and partner with team members on Big Data and Cloud Tech stacks.

• Familiarity with containerization tools such as Docker and Kubernetes.

• Clear communication skills with the ability to work effectively across technical and non-technical teams.

Beneficial Skills & Qualifications

• Experience using Snowflake/Databricks for cloud data warehousing and analytics.

• Exposure to migrating or modernizing legacy data platforms, including SAS-based workflows.

• Familiarity with machine learning data pipelines and MLOps concepts.

• Development experience in Scala or Java alongside Python.

• AWS or Google Cloud Platform cloud certification(s).

At Citi, you’ll work as part of a collaborative engineering team on data challenges that have direct business impact across a global organization. This role offers meaningful technical development, exposure to modern cloud and data technologies, and a structured path to grow your career within Citi Technology.

• Hybrid working model with 3 days in the office and 2 days working remotely, giving you flexibility alongside regular in-person collaboration.

• Hands-on experience with modern cloud platforms, Big Data technologies, and analytics tooling used at enterprise scale.

• Structured learning and development opportunities to support your technical growth and career progression within Citi Technology.

• Exposure to data modernization projects that directly support business decision-making across a global financial institution.

• Competitive compensation package and comprehensive benefits program.