Big Data Engineer - Scala, - Py spark, -Spark

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

Big Data Engineer - Scala, - Py spark, -Spark

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 at least one of the

data integration platforms such as Ab Initio, Apache Spark, Talend and Informatica

Big Data

Exposure to ‘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)

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

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.

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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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