Tech Lead, Data Engineering

Job description

**Be Part of Our Next Chapter** For over almost 60 years, our solutions have enabled impactful connections between some of the world’s leading brands and their customers. And while we’ve already done a lot of work we’re proud of, we’re just getting started! We’re a global technology company focused on creating dynamic, smart, personalized and engaging customer experiences powered by our range of digital hardware, our proprietary content management system and our industry leading signage solutions. (For example: If you’ve ordered in-store or in the drive-thru at McDonald’s somewhere in the world in the last few years, chances are you’ve interacted with our digital solutions.) We work in over 50 global markets and have 9 offices around the world, with a global headquarters proudly located in our founding home of Sydney, Australia. Coates Group has the values of a family-owned business and the innovative spirit of a start-up, both which fuel our purpose – *Creating Connections. Empowering Partnerships. Always Evolving*. Through hard work, dedication and creativity, we’ve become industry leaders who have won awards and set records while remaining focused on continual growth and evolution. We are a 2x Australia Good Design Award winner and successfully completed the largest hardware deployment in Quick Service Restaurant history. We are curious, charismatic, authentic and we value and leverage the diversity of our crew. We are imaginers, kindness enthusiasts, experts, creators, thinkers, challengers, collaborators and over-achievers. And together, as a Crew, we are revolutionizing the way the world’s leading brands leverage technology to drive the best customer experiences. Design, build, and lead the evolution of Coates’ next-generation enterprise data platform on AWS, establishing scalable and secure foundations for how data is integrated, governed, transformed, and consumed across the organization. This role enables business intelligence, analytics, operational decision-making, and future AI capabilities through hands-on technical leadership, architecture design, and cross-functional partnership. ### Responsibilities •Architect and evolve a scalable, cost-efficient AWS data platform that enables enterprise analytics, reporting, and future AI capabilities •Design and implement reliable, production-grade data pipelines and processing frameworks for batch and near real-time data •Define enterprise standards for data architecture, modeling, observability, reliability, and platform performance • Lead foundational technical decisions across tooling, infrastructure, data design, scalability, and operational efficiency •Implement and maintain infrastructure-as-code, CI/CD pipelines, and automated deployment practices across the data platform •Enable high-quality, accessible, and governed data products through scalable semantic layers, curated datasets, and transformation standards •Provide hands-on technical leadership, mentoring, and architectural direction for a growing team of data engineers and cross-functional stakeholders ### Capabilities •Ability to design scalable, secure, and cost-efficient data architectures that support enterprise analytics and future AI initiatives •Strong technical problem-solving and decision-making skills, including balancing tradeoffs between scalability, complexity, performance, and cost •Ability to establish engineering standards, operational best practices, and reliable platform governance processes •Strong understanding of CI/CD pipelines, modern software engineering practices, and production support models •Experience enabling accessible, high-quality data products for analytics, reporting, and downstream business consumers •Ability to work collaboratively across technical and business stakeholders while providing hands-on technical leadership and direction •Exposure to streaming technologies (e.g., Kafka or Kinesis), transformation frameworks such as dbt, multi-cloud environments, and/or ML and data science workflows is beneficial ### Qualifications •6+ years of experience in data engineering with strong expertise in scalable system and platform design •Proven experience building, modernizing, or significantly evolving enterprise data platforms and architectures •Deep hands-on experience within the AWS data ecosystem, including production-scale data lake or lakehouse environments utilizing technologies such as S3, Glue, Spark, Athena, and/or Redshift •Strong proficiency in Python and SQL with experience developing production-grade data pipelines and transformation workflows •Experience implementing workflow orchestration solutions, preferably Airflow •Experience with infrastructure-as-code and automated deployment practices using tools such as Terraform or AWS CDK •Familiarity with modern data warehousing and lakehouse concepts, including dimensional modeling and platforms such as Snowflake, Redshift, or Databricks