The Data Engineer is accountable for developing high quality data products to support the Bank’s regulatory requirements and data driven decision making. A Data Engineer will serve as an example to other team members, work closely with customers, and remove or escalate roadblocks. By applying their knowledge of data architecture standards, data warehousing, data structures, and business intelligence they will contribute to business outcomes on an agile team.
Responsibilities
- Developing and supporting scalable, extensible, and highly available data solutions
- Deliver on critical business priorities while ensuring alignment with the wider architectural vision
- Identify and help address potential risks in the data supply chain
- Follow and contribute to technical standards
- Design and develop analytical data models
Required Qualifications & Work Experience
- First Class Degree in Engineering/Technology (4-year graduate course)
- 7 to 12 years’ experience implementing data-intensive solutions using agile methodologies
- Experience of relational databases and using SQL for data querying, transformation and manipulation
- Experience of modelling data for analytical consumers
- Ability to automate and streamline the build, test and deployment of data pipelines
- Experience in cloud native technologies and patterns
- A passion for learning new technologies, and a desire for personal growth, through self-study, formal classes, or on-the-job training
- Excellent communication and problem-solving skills
- An inclination to mentor; an ability to lead and deliver medium sized components independently
Technical Skills (Must Have)
ETL: Design, develop, and maintain scalable and efficient data processing pipelines using Python and Spark.
Big Data: Experience of ‘big data’ platforms such as Hadoop, Hive for data storage and processing.
Data Warehousing & Database Management: Understanding of Data Warehousing concepts, Any Relational Databases (Oracle, MSSQL, MySQL) including SQL & performance tuning.
Data Modeling & Design: Good exposure to data modeling techniques; design, optimization and maintenance of data models and data structures
Languages: Proficient in Python programming language.
DevOps: Exposure to concepts and enablers - CI/CD platforms, version control, automated quality control management.
Technical Skills (Valuable/Good to Have)
- ETL: Design, develop, and maintain scalable and efficient data processing pipelines using Python and Spark.
- Big Data: Experience of ‘big data’ platforms such as Hadoop, Hive for data storage and processing.
- Data Warehousing & Database Management: Understanding of Data Warehousing concepts, Any Relational Databases (Oracle, MSSQL, MySQL) including SQL & performance tuning.
- Data Modeling & Design: Good exposure to data modeling techniques; design, optimization and maintenance of data models and data structures
- Languages: Proficient in Python programming language.
- DevOps: Exposure to concepts and enablers - CI/CD platforms, version control, automated quality control management.
- Cloud: Experience with AWS Cloud Platform.
- DevOps: Experience with CI/CD Tools like LSE (Light Speed Enterprise), Jenkins, GitHub.
- Certification on any of the above topics would be an advantage
- Data Quality & Controls: Exposure to data validation, cleansing, enrichment and data controls
- Containerization: Fair understanding of containerization platforms like Docker, Kubernetes
- File Formats: Exposure in working on Event/File/Table Formats such as Avro, Parquet, Protobuf, Iceberg, Delta
- Others: Experience of using a Job scheduler e.g., Autosys. Exposure to Business Intelligence tools e.g., Tableau, Power BI
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Job Family Group:
Technology------------------------------------------------------
Job Family:
Digital Software Engineering------------------------------------------------------
Time Type:
Full time------------------------------------------------------
Most Relevant Skills
Please see the requirements listed above.------------------------------------------------------
Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.------------------------------------------------------
Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.
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View Citi’s EEO Policy Statement and the Know Your Rights poster.
Skills Required
- First Class Degree in Engineering or Technology (4-year graduate course)
- 7 to 12 years experience implementing data-intensive solutions using agile methodologies
- Experience with relational databases and using SQL for data querying, transformation and manipulation
- Experience modeling data for analytical consumers
- Ability to automate and streamline build, test and deployment of data pipelines (CI/CD)
- Experience in cloud-native technologies and patterns
- Excellent communication and problem-solving skills
- Inclination to mentor; ability to lead and deliver medium sized components independently
- ETL: design, develop and maintain data processing pipelines using Python and Spark
- Big Data platforms such as Hadoop and Hive
- Data warehousing and relational database management (Oracle, MSSQL, MySQL) including SQL and performance tuning
- Data modeling and design techniques, optimization and maintenance of data models and structures
- Proficient in Python programming language
- DevOps exposure: CI/CD platforms, version control, automated quality control management
- Experience with AWS Cloud Platform
- CI/CD tools such as LSE (Light Speed Enterprise), Jenkins, GitHub
- Certifications on related technologies (advantage)
- Experience with data quality, validation, cleansing and data controls
- Containerization platforms like Docker and Kubernetes
- Experience with file/event formats: Avro, Parquet, Protobuf, Iceberg, Delta
- Experience using a job scheduler (e.g., Autosys)
- Exposure to BI tools such as Tableau or Power BI
Citi Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Citi and has not been reviewed or approved by Citi.
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Healthcare Strength — Benefits coverage is positioned as comprehensive, including health, dental, and vision insurance plus on-site clinics, prescription drug support, and disability coverage. Family-building support such as fertility assistance is described as a notable differentiator within the overall package.
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Retirement Support — Retirement benefits are framed as strong, highlighted by a 401(k) with matching and additional plan options like a Roth 401(k). Financial support is reinforced through discounts and broader financial guidance resources tied to the benefits ecosystem.
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Wellbeing & Lifestyle Benefits — Wellbeing support extends beyond insurance through programs like an Employee Assistance Program, counseling/legal resources, and gym or wellness reimbursement. These offerings increase the perceived total rewards value even when cash compensation sentiment varies by role.
Citi Insights
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