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Atlassian is looking for a Principal Data Engineer to join our Corporate Data Engineering team which is responsible for building our data lake, maintaining our big data pipelines / services and facilitating the movement of billions of messages each day. We work directly with the business stakeholders and plenty of platform and engineering teams to enable growth and retention strategies at Atlassian. We are looking for an open-minded, structured thinker who is passionate about building services that scale.
Job Responsibility:
help our stakeholder teams ingest data faster into our data lake
find ways to make our data pipelines more efficient
come up ideas to help instigate self-serve data engineering within the company
building micro-services, architecting, designing, and enabling self serve capabilities at scale to help Atlassian grow
Requirements:
12+ years of experience in a Data Engineer role as an individual contributor
at least 2 years of experience as a tech lead for a Data Engineering team
track record of driving and delivering large and complex efforts
great communicator and maintain cross-team and cross-functional relationships
experience with building streaming pipelines with a micro-services architecture for low-latency analytics
experience working with varied forms of data infrastructure, including relational databases (e.g. SQL), Spark, and column stores (e.g. Redshift)
experience building scalable data pipelines using Spark using Airflow scheduler/executor framework or similar scheduling tools
experience working in a technical environment with the latest technologies like AWS data services (Redshift, Athena, EMR) or similar Apache projects (Spark, Flink, Hive, or Kafka)
understanding of Data Engineering tools/frameworks and standards to improve the productivity and quality of output for Data Engineers across the team
industry experience working with large-scale, high-performance data processing systems (batch and streaming) with a "Streaming First" mindset
Nice to have:
Experienced with data architecture across Enterprise CRM, Commerce and Finance Systems, and building solid data foundations
Built pipelines using Databricks and is well-versed with their APIs
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