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
Required Qualifications & Work Experience
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
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
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: 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.
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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