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)
- 12 to 14 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)
- Big Data: Experience of ‘big data’ platforms such as Hadoop, Hive or Snowflake for data storage and processing, Spark / Pyspark.
- Data Warehousing & Database Management: Expertise around 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
- Data Governance: A strong grasp of principles and practice including data quality, security, privacy and compliance
- Cloud: Good exposure to public cloud data platforms such as AWS, Databricks, etc. Demonstratable understanding of underlying architectures and trade-offs
Technical Skills (Valuable)
- Data Quality & Controls: Exposure to data validation, cleansing, enrichment and data controls
- Containerization: Fair understanding of containerization platforms like Docker, Kubernetes
- Preferably good to have experience on Pyspark/AWS/Databricks
- 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
Certification on any one or more 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
For complementary skills, please see above and/or contact the recruiter.------------------------------------------------------
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