Data Engineer
Content + Source + Freshness • 15 Dec 2025 • 95% confidence
Offer value
High potential for impact on business through data products, with strong backing from an established financial institution.
Pros
- Direct contribution to regulatory compliance and decision-making.
- Focus on high-quality deliverables within agile frameworks.
- Continuous learning and adaptation to new platforms.
Cons
- Expectations for automation and efficiency may add pressure.
- Specific skill set required might limit applicants.
- Potential lack of work-life balance due to project demands.
Who it is for
Mid-level to Advanced • On-site
Good fit
- Experienced professionals with 5-8 years in data engineering.
- Applicants skilled in data modeling and ETL processes.
- Individuals experienced in agile environments.
Not recommended for
- Entry-level candidates without relevant engineering experience.
- Those seeking fully remote roles.
- Candidates without strong organizational skills.
Motivation fit
Key skills
About the job
The Role
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)
- 5 to 8 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
Technical Skills (Must Have)
- ETL: Hands on experience of building data pipelines. Proficiency in two or more data integration platforms such as Ab Initio, Apache Spark, Talend and Informatica
- Big Data: Experience of ‘big data’ platforms such as Hadoop, Hive or Snowflake for data storage and processing
- Data Warehousing & Database Management: Understanding of Data Warehousing concepts, Relational (Oracle, MSSQL, MySQL) and NoSQL (MongoDB, DynamoDB) database design
- Data Modeling & Design: Good exposure to data modeling techniques; design, optimization and maintenance of data models and data structures
- Languages: Proficient in one or more programming languages commonly used in data engineering such as Python, Java or Scala
- DevOps: Exposure to concepts and enablers - CI/CD platforms, version control, automated quality control management
Technical Skills (Valuable)
- Ab Initio: Experience developing Co>Op graphs; ability to tune for performance. Demonstrable knowledge across full suite of Ab Initio toolsets e.g., GDE, Express>IT, Data Profiler and Conduct>IT, Control>Center, Continuous>Flows
- Cloud: Good exposure to public cloud data platforms such as S3, Snowflake, Redshift, Databricks, BigQuery, etc. Demonstratable understanding of underlying architectures and trade-offs
- 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: Basics of Job scheduler like Autosys. Basics of Entitlement management
- Certification on any of the above topics would be an advantage.
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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
Structured Query Language (SQL).------------------------------------------------------
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