Digital S/W Eng Intm Analyst

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Job Overview

The Digital S/W Engineer Intmd Analyst is a developing professional role and 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.