- Build scalable AWS data pipelines.
- Develop solutions using SQL & Python.
- Deliver trusted data for analytics & reporting.
Company Description
Showtime Consulting is a leading provider of Shielded Cloud and Digital Solutions across Australia and New Zealand. We specialise in delivering secure, enterprise-scale technology solutions across cloud, data, DevSecOps, platform engineering, and digital transformation programs.
We partner with major organisations to build high-performing technology teams and deliver innovative data and cloud solutions that enable better decision-making, operational efficiency, and business transformation.
The Role
We are seeking an experienced Senior AWS Data Engineer to join a high-performing enterprise data team.
This is a hands-on role focused on designing, building, and supporting scalable data pipelines and curated data products on AWS. You will play a key role in delivering reliable, high-quality data solutions that support reporting, analytics, operational processes, and downstream applications across a large-scale enterprise environment.
The ideal candidate will bring strong expertise in AWS data services, advanced SQL development, Python programming, and modern data engineering practices. You will work closely with business stakeholders, analysts, data scientists, and platform teams to deliver robust and scalable data solutions while contributing to DataOps and continuous improvement initiatives.
Key Responsibilities
- Design, develop, and maintain end-to-end data pipelines across batch and near real-time data processing environments.
- Build and manage ETL/ELT workflows using AWS data services including Glue, S3, Redshift, Athena, and EMR.
- Develop and maintain scalable transformation frameworks using SQL, Python, PySpark, and dbt.
- Implement data ingestion solutions from multiple source systems including databases, APIs, flat files, and event-driven platforms.
- Design and manage data lake and warehouse layers including raw, cleansed, and curated datasets.
- Develop robust data quality frameworks including validation rules, reconciliation controls, monitoring, and alerting capabilities.
- Collaborate with data analysts, reporting teams, and data scientists to develop analytics-ready datasets and support machine learning initiatives.
- Optimise data pipeline performance, scalability, and reliability across enterprise data platforms.
- Support workflow orchestration and scheduling through Airflow and other automation tools.
- Contribute to DataOps and DevOps practices including source control, CI/CD pipelines, automated testing, release management, and deployment processes.
- Troubleshoot production issues and provide operational support for critical data workloads.
- Produce and maintain technical documentation including data mappings, workflow diagrams, runbooks, operational procedures, and support documentation.
- Engage with business and technology stakeholders to translate requirements into scalable data solutions.
What You'll Need
- Experience in Data Engineering within enterprise-scale environments.
- Advanced SQL skills, including query optimisation, performance tuning, complex joins, CTEs, and window functions.
- Strong hands-on experience building and supporting data pipelines within large-scale data platforms.
- Extensive experience with AWS data services, including:
- Amazon S3
- AWS Glue
- Amazon Redshift
- Amazon Athena
- Amazon EMR
- Strong experience with dbt and modern ELT development practices.
- Advanced programming skills in Python and hands-on experience developing data processing solutions using PySpark and/or Apache Spark.
- Experience working with workflow orchestration platforms such as Apache Airflow.
- Experience implementing data quality controls, monitoring, logging, alerting, and operational support processes.
- Strong understanding of data warehousing concepts including dimensional modelling, partitioning strategies, incremental loading, and Change Data Capture (CDC).
- Hands-on experience working with Teradata and Siebel CRM datasets.
- Experience collaborating with business stakeholders and translating requirements into technical data solutions.
- Strong problem-solving, communication, and stakeholder engagement skills.
Desirable Skills
- Telecommunications industry experience.
- Experience working within enterprise-scale cloud and data transformation programs.
- Knowledge of Lakehouse architectures and modern data platform design principles.
- Experience with CI/CD pipelines and automated deployment frameworks.
- Familiarity with DataOps best practices and operational governance frameworks.
- Experience working with near real-time or event-driven data processing platforms.
- Exposure to machine learning and analytics data preparation requirements.
Why Join Us?
- Work on large-scale enterprise data and cloud transformation initiatives.
- Build modern AWS-based data platforms supporting critical business services and analytics capabilities.
- Gain exposure to cutting-edge data engineering technologies and best practices.
- Collaborate with experienced cloud, data, platform, and engineering specialists.
- Influence data engineering standards, architecture, and platform direction.
- Deliver high-impact solutions that enable data-driven decision-making across the organisation.
