At IBM, work is more than a job - it's a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you've never thought possible. Are you ready to lead in this new era of technology and solve some of the world's most challenging problems? If so, lets talk.
We are currently hiring a Data Engineer for full-time position, based in Sydney, Australia.
The Data Engineers in the Nexus-Ops team are responsible for supporting the Data Harbour program, through working with stakeholders including Architects, technical teams, Business users, and data producers, to understand the target state requirements including upstream and downstream use cases. As a senior member of the Data Engineer team, the Lead Data Engineer will be accountable for end-to-end architecture, design, and development process, including providing technical and architecture advisory support to the implementation team and the wider Macquarie stakeholders. As the Lead Data Engineer your responsibilities include –
Architect, Design and Solutioning cloud based big data engineering and analytics projects.
Architect, Design data pipeline for batch, Streaming, Realtime data using AWS and Spark.
Creating architecture, design, technical specifications artefacts.
Participate in depth architectural discussions to build confidence and ensure customer success when building new solutions and migrating existing data applications on the AWS platform.
Apply in depth experience in AWS cloud services and hands on implementation experience using data-based services on AWS.
Work with a variety of cloud-based storage services: AWS S3, Elastic Storage, RDS, AWS Data pipeline, AWS Glue and Data analytics services.
Abide by team coding standards, development processes and best practices.
To ensure success in the role you will possess the following skills –
Must have been through several full-lifecycle Big Data engineering implementations and involved in availability, scalability and performance related design aspects in Data processing and Reporting.
Strong hands-on experience working in AWS Serverless (Lambda), Coding against API and SQL.
Must be hands-on with writing Python or Scala Scripts development.
Hands on experience designing of orchestration using Airflow.
Well versed with Spark development (SQL and/or Spark).
Hands on experience in AWS Service EC2, S3, SNS, SQS, Lambda, Hue, Presto, Spark, Glue, Kinesis, EMR, ECS, EKS.
Sound knowledge in Hive, Redshift, AWS RDS, My SQL and in any NoSQL database e.g., Cassandra, MongoDB.
Experience in architecting and designing batch or real time data streaming.
Ability to dynamically adapt to conventional big data frameworks and opensource tools if project demands.
Knowledge of design strategies for developing scalable, resilient, always-on data lake.
Strong development and automation skills.
Good understanding in data architecture principles, including data access patterns, data modelling and strong in Data base concepts.
Previous experience in Managed Services related reporting in a financial services organization is an advantage.