The Data Engineer is accountable for developing high quality data products to support theBank’s regulatory requirements and data driven decision making. A Data Engineer will serveas an example to other team members, work closely with customers, and remove orescalate roadblocks. By applying their knowledge of data architecture standards, datawarehousing, data structures, and business intelligence they will contribute to businessoutcomes on an agile team.
Responsibilities: -
Developing and supporting scalable, extensible, and highly available data solutionsDeliver on critical business priorities while ensuring alignment with the widerarchitectural visionIdentify and help address potential risks in the data supply chainFollow and contribute to technical standardsDesign and develop analytical data models
Required Qualifications & Work Experience
- First Class Degree in Engineering/Technology (4-year graduate course)
- 9 to 11 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:
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 underlyingarchitectures and trade-offs
Data Quality & Controls:
Exposure to data validation, cleansing, enrichment anddata 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 BusinessIntelligence tools e.g., Tableau, Power BICertification 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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