Amazon32 followers154 jobs

Data Engineer

Mid levelFull-timeHybridMelbourne VIC, AustraliaDataInformation technologySoftware engineeringPosted 1 month ago
View your fit0 of 9 criteria met

About the job

As a Data Engineer, you will play a crucial role in enabling data-driven decision making within the Amazon Web Services (AWS) Data Center Infrastructure Operations organization. This job offers an exciting opportunity to work with huge data sets and emerging technologies, driving business intelligence solutions from end to end. The team thrives on collaboration and inclusivity, empowering each member to contribute bold ideas and take ownership of their projects.

You'll be responsible for

🤝

Supporting teams

Primarily supporting teams within the Infrastructure environment, with long-term opportunities to support teams in the overall AWS community.
🛠️

Building ETLs

Building ETLs to ingest data into the data warehouse and data lake, as well as end-user facing reporting applications.
📈

Developing metrics and reports

Developing, implementing, and maintaining metrics and reports to enable decision support systems for the organization.

Skills you'll need

📊

Data engineering experience

4+ years of data engineering experience, enabling data-driven decision making.
🔄

ETL pipeline building

Experience with data modeling, warehousing, and building ETL pipelines to ingest data into data warehouses and data lakes.
☁️

AWS technologies

Familiarity with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions.
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Meet the team

About the company

View your fit

0 of 5 criteria met
4+ years of data engineering experience
Demonstrated experience in data engineering roles is essential.

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ETL pipeline development
Proven skills in building ETL pipelines for data ingestion.

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AWS technology proficiency
Experience with AWS services like Redshift and S3 is crucial.

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Big data technology experience
Familiarity with Hadoop, Spark, and similar technologies is required.

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Data modeling and warehousing
Knowledge in data modeling and warehousing practices is necessary.

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