Senior Data Engineer

3 months ago

Melbourne, Australia carsales Full time

What’s on offer

Autonomy to choose where you work from – Office, Hybrid or Anywhere. We’re proud to be recognised as one of Australia’s Best Workplaces™ by Great Place to Work®, a Family Friendly Workplace, a WORK endorsed employer for women and WGEA Employer of Choice for Gender Equality. Support of a highly engaged, high-performing team. We have incredible talent in carsales that you will learn from. 22 weeks paid parental leave for primary caregivers, four weeks paid secondary caregivers leave, six weeks paid gender affirming care leave. Regular Hackathons, endless learning and development opportunities, and a range of activities that will help support your mental, emotional, and physical wellbeing.

Job Description

What you’ll do

Contributing to the delivery of scalable data architectures, and development & design best practices Leading collaborations across data disciplines to develop, optimise and maintain data pipelines and solutions Engages actively in facilitating team-based problem-solving sessions and contribute to the development of best practices Initiating and nurturing effective working relationships, acting as a trusted advisor on product analytics and commercial data solutions Leading technical recommendations and decision-making while, mentoring early-career engineers playing a key role in growing the team's capabilities Owning the delivery of their allocated initiatives within specified scope, times and budgets

Qualifications

What we are looking for?

Critical to success in the role is the ability to operate in the liminal space between business, data and technical practice.

An all-of-business ownership mindset over siloed success; leading with high levels of personal integrity and accountability Ability to distil business and analytics requirements into well-defined engineering problems Skilled at identifying appropriate software engineering methods (e.g. modularisations, abstractions) that make data assets tractable Strong software engineering fundamentals (e.g. data structures, principles of software design, build & testing) Strong data engineering experience (e.g. transformations, modelling, pipelines), grounded in the basics of an analytical discipline (e.g. analytics or science) Skilled in designing and building pipelines using cloud services such as AWS EC2, Glue, Lambda, SNS, SQS, IAM, ECS or equivalent Demonstrated experience with distributed technologies such as Airflow, HDFS, EMR Proficient in two or more programming languages such as Python, Spark, Scala or similar

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