Senior Data Engineer

Overview

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. Interviews and onboarding are conducted virtually, a part of being a distributed-first company.

Responsibilities

Team: Core Engineering Reliability Team

• Collaborate with engineering and TPM leaders, developers, and process engineers to create data solutions that extract actionable insights from incident and post-incident management data, supporting objectives of incident prevention and reducing detection, mitigation, and communication times.

• Work with diverse stakeholders to understand their needs and design data models, acquisition processes, and applications that meet those requirements.

• Add new sources, implement business rules, and generate metrics to empower product analysts and data scientists.

• Serve as the data domain expert, mastering the details of our incident management infrastructure.

• Take full ownership of problems from ambiguous requirements through rapid iterations.

• Enhance data quality by leveraging and refining internal tools and frameworks to automatically detect issues.

• Cultivate strong relationships between teams that produce data and those that build insights.

Qualifications

Minimum Qualifications / Your background:

• BS in Computer Science or equivalent experience with 8+ years as a Senior Data Engineer or similar role

• 10+ Years of progressive experience in building scalable datasets and reliable data engineering practices.

• Proficiency in Python, SQL, and data platforms like DataBricks

• Proficiency in relational databases and query authoring (SQL).

• Demonstrable expertise designing data models for optimal storage and retrieval to meet product and business requirements.

• Experience building and scaling experimentation practices, statistical methods, and tools in a large scale organization

• Excellence in building scalable data pipelines using Spark (SparkSQL) with Airflow scheduler/executor framework or similar scheduling tools.

• Expert experience working with AWS data services or similar Apache projects (Spark, Flink, Hive, and Kafka).

• Understanding of Data Engineering tools/frameworks and standards to improve the productivity and quality of output for Data Engineers across the team.

• Well versed in modern software development practices (Agile, TDD, CICD)

Desirable Qualifications

• Demonstrated ability to design and operate data infrastructure that deliver high reliability for our customers.

• Familiarity working with datasets like Monitoring, Observability, Performance, etc..