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A BS in Computer Science or equivalent experience
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At least 5+ years professional experience as a Software Engineer or Data Engineer
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Strong programming skills (Python, Java or Scala preferred)
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Experience writing SQL, structuring data, and data storage practices
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Experience with data modeling
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Knowledge of data warehousing concepts
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Experience building data pipelines, platforms
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Experience with Databricks, Spark, Hive, Airflow and other streaming technologies to process incredible volumes of streaming data
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Experience in modern software development practices (Agile, TDD, CICD)
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Strong focus on data quality and experience with internal/external tools/frameworks to automatically detect data issues, anomalies.
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A willingness to accept failure, learn and try again
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An open mind to try solutions that may seem crazy at first
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Experience working on Amazon Web Services (in particular using EMR, Kinesis, RDS, S3, SQS and the like)
Its preferred that you have:
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Experience building self-service tooling and platforms
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Built and designed Kappa architecture platforms
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Contributed to open source projects (Ex: Operators in Airflow)
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Experience with Data Build Tool (DBT)
As a Data Engineer, you will have the opportunity to apply your strong technical experience building highly reliable services on managing and orchestrating a multi-petabyte scale data lake. You enjoy working in a fast paced environment and you are able to take vague requirements and transform them into solid solutions. You are motivated by solving challenging problems, where creativity is as crucial as your ability to write code and test cases.
Below are the key responsibilities:
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Partner with Product Managers, Engineering, analytics, and business teams to review and gather the data/reporting/analytics requirements and build trusted and scalable data models, data extraction processes, and data applications to help answer complex questions.
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Design and implement data pipelines to ETL data from multiple sources into a central data warehouse.
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Improve data quality by using and improving internal tools/frameworks to automatically detect DQ issues.
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Develop and implement data governance procedures to ensure data security, privacy, and compliance.
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Implement new technologies to improve data processing and analysis.
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Coach junior data engineers to improve their skills.
Working at Atlassian
Atlassians can choose where they work - whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity.
Joining our Data Engineering team means working alongside highly capable, candid colleagues who challenge each other to think deeply and raise the bar on execution. We value strong individual ownership, but collaboration is core to how we operate. Our mission is to enable growth by building reliable data foundations and delivering accurate, actionable insights to teams across the organization.
