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Day 114
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Pyrmont, NSW, AU
Open 3 months, first seen by us 114 days agoEmployment type in the employer’s posting: FULL_TIMEFrom the employer’s posting
Design and build reusable, scalable data solutions on a Lakehouse platform
Build and maintain batch pipelines using DBT, Airflow, Snowflake, Databricks and AWS
Develop real-time pipelines with Apache Kafka and Apache Flink
Build and optimise Apache Iceberg tables, including schema and partitioning
Connect data from SaaS, APIs, direct connections and streaming sources
Design and build re-usable, scalable data solutions and assets on a modern Lakehouse platform
Build and maintain robust, reliable pipelines using DBT, Apache Airflow, Snowflake, Databricks and AWS services.
Develop real-time data pipelines using Apache Kafka and Apache Flink
Build and optimise open Lakehouse table formats such as Apache Iceberg, managing schema evolution, partitioning, and performance.
Connect to a wide variety of internal and external sources via multiple methods (SaaS solutions, APIs, direct connections, streaming) on cloud architecture.
Open 3 monthsWe first saw this posting 114 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 114
Up longer than most. Similar jobs usually come down between day 5 and day 54.
From 10,733 similar postings that came down. Coming down is not always a hire.
25 open jobs
Open jobs by the week we first saw them, last 12 weeks.
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Essential: * At least 5+ years of hands-on commercial experience in software or data engineering. * 4+ years of strong development experience in SQL and Python. * Proven experience with data integration and orchestration tools, including Apache Airflow, DBT, and AWS Glue. * Expert on some of the modern cloud data platforms and architectures, such as Snowflake, Databricks, and Lakehouse architecture * Experience designing and building data solutions on AWS cloud infrastructure. * Experience working with semi-structured and unstructured data. * Strong data modelling skills with the ability to evolve models in an agile environment. * Hands-on experience building streaming and real-time pipelines with Apache Kafka and Apache Flink. * Knowledge of infrastructure-as-code tools such as CDK, CloudFormation, or Terraform. * Degree in computer science, engineering, mathematics, statistics, or a related technical field What’s In It For You?
What you’ll do * Solution Design: Design and build re-usable, scalable data solutions and assets on a modern Lakehouse platform across a constantly evolving landscape of products and internal data teams. * Pipeline Development: Build and maintain robust, reliable pipelines using DBT, Apache Airflow, Snowflake, Databricks and AWS services. * Streaming & Real-Time: Develop real-time data pipelines using Apache Kafka and Apache Flink to support low-latency, event-driven use cases. * Data Integration: Connect to a wide variety of internal and external sources via multiple methods (SaaS solutions, APIs, direct connections, streaming) on cloud architecture. * Cloud Data Architecture: Architect and implement data solutions on AWS, Snowflake and Databricks, applying best practices for cloud-native data warehousing and cost-efficiency. * Lakehouse Architecture: Build and optimise open Lakehouse table formats such as Apache Iceberg, managing schema evolution, partitioning, and performance. * Modern Data Modelling: Apply modern data modelling practices that evolve with the business in an agile environment. * Performance & Cost: Write and optimise complex SQL and Spark workloads to keep solutions efficient in both time and cost. * Data quality and governance * Cross-functional Partnership: Work closely with data scientists, analysts, product managers, and engineers across the Domain Group to deliver data solutions that drive business value. * Continuous Improvement: Challenge the status quo — proactively identify opportunities to improve data processes, reduce costs, and increase reliability. Essential:
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We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
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Pyrmont, NSW, AU
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