Senior Data Engineer - Pyspark - Assistant Vice President

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)
• 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 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

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Job Family Group:

Technology

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Job Family:

Digital Software Engineering

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Time Type:

Full time

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Most Relevant Skills

Please see the requirements listed above.

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Other Relevant Skills

For complementary skills, please see above and/or contact the recruiter.

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