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Design Bronze, Silver and Gold medallion layers for trusted data
Model batch and streaming datasets into reusable, governed data models
Build Databricks pipelines with Delta Lake, DLT or Lakeflow
Implement streaming ingestion with Spark Structured Streaming and Kafka
Strengthen governance with cataloging, lineage and data quality checks
Design trusted Bronze, Silver and Gold layers using medallion architecture principles
Model large-scale batch and streaming datasets into reusable, governed data models for reporting use cases
Build Databricks pipelines using Delta Lake, Delta Live Tables or Lakeflow aligned to quality and lineage needs
Implement streaming ingestion and processing using Spark Structured Streaming and Kafka or similar
Strengthen governance with cataloging, lineage and data quality checks using Unity Catalog or equivalent patterns
Actively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
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Day 0
Early. Similar jobs usually come down between day 6 and day 54.
From 11,508 similar postings that came down. Coming down is not always a hire.
660 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordAdd your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
We are seeking a Senior Data Engineer (Databricks, Data Modeling) . You will model large-scale batch and streaming data into governed Bronze, Silver and Gold layers to support regulatory reporting data products. You will build on Databricks using Spark, Python and SQL, partner with stakeholders on definitions and use AI-assisted tools to speed up delivery, testing and documentation. Responsibilities Design trusted Bronze, Silver and Gold layers using medallion architecture principles Model large-scale batch and streaming datasets into reusable, governed data models for reporting use cases Build Databricks pipelines using Delta Lake, Delta Live Tables or Lakeflow aligned to quality and lineage needs Develop Spark workloads using PySpark and Spark SQL for performance, reliability and maintainability Implement streaming ingestion and processing using Spark Structured Streaming and Kafka or similar Partner with business and regulatory stakeholders to translate reporting needs into data products and model definitions Strengthen governance with cataloging, lineage and data quality checks using Unity Catalog or equivalent patterns Contribute to CI/CD for data pipelines, Git-based workflows, testing and release practices Requirements Data modeling background across conceptual, logical and physical models using dimensional, Data Vault or similar approaches Databricks experience, including Delta Lake plus governance concepts such as Unity Catalog Apache Spark skill sets using PySpark and Spark SQL in production environments Streaming data experience using Spark Structured Streaming, Kafka or similar technologies Python capability for pipeline development and orchestration patterns Advanced SQL skill sets for transformation logic, validation and performance tuning Data governance experience covering quality, lineage, controls and well-defined data contracts AI-assisted development tooling exposure, with Claude preferred, to accelerate build, testing and documentation Nice to have Regulatory reporting data exposure such as EMIR, MiFIR or ASIC derivative reporting Data mesh experience delivering domain-owned data products Data Vault 2.0 experience in large-scale enterprise models Apache Kafka experience and strong understanding of streaming platform operations
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New Data Engineer postings at the employers we follow are up 8% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
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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Australia
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