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Atlassian Off Campus Hiring 2025 Data Engineer . This is a great opportunity for freshers and early-career professionals looking to kickstart their journey in a global IT services company. The position is based in Gurugram , Maharashtra, India and eligible candidates are encouraged to apply online at the earliest. The detailed eligibility criteria and application process are given below. Also Apply All Trainee Active Jobs For Freshers
About Atlassian
Atlassian is a “distributed‑first” software company whose mission is to “unleash the potential of every team.” Its well‑known products—Jira, Confluence, Trello, Bitbucket and others—help teams plan, build and run projects of every kind. Employees can work remotely, from an office, or a mix of both in any country where Atlassian has a legal entity, reflecting the firm’s Team Anywhere model. Diversity, inclusion and equal opportunity are foundational; the company explicitly welcomes applicants of every background and offers tailored accommodations throughout the hiring process.
Job Overview
Atlassian is looking for a Data Engineer to join our Data Engineering team, responsible for building our data lake, maintaining big data pipelines / services and facilitating the movement of billions of messages each day. We work directly with the business stakeholders, 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/pipelines that scale.
You will join the Data Engineering group inside the Analytics & Data Science organization. The team owns Atlassian’s multi‑petabyte data lake and the large‑scale pipelines that carry billions of events each day. Your core goal is to make high‑quality data available—quickly and reliably—so product, growth and business teams can experiment, measure and decide faster.
Key Responsibilities
- Build & maintain pipelines: Design, code and operate batch (Spark + Airflow) and streaming (Kafka/Spark Streaming) jobs that ingest, transform and surface data.
- Scale the data lake: Improve performance, cost efficiency and observability as data volume and complexity grow.
- Enable self‑service analytics: Create models, templates and tooling that let non‑engineers load and query data safely.
- Partner with stakeholders: Work directly with product managers, analysts and other engineering teams to map requirements, define metrics and ship trustworthy datasets.
- Drive continuous improvement: Identify gaps, propose architectural changes and champion data‑engineering best practices across Atlassian.