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Senior Engineering Manager, Micros Foundations

Company: Atlassian

Location: Other US Location

Posted Mar 01, 2025

Atlassian is seeking a Senior Engineering Manager to lead a team of Backend Software Engineers. The role involves guiding technical decisions, prioritizing work, and providing career growth opportunities. The manager will work closely with architecture and product management teams to build secure, reliable, and compliant systems. Key responsibilities include driving cultural change, managing large projects, and partnering across engineering teams. The ideal candidate should have a track record of leading high-performing teams, experience in agile methodologies, and proficiency in cloud services and containerized workloads.

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Entry‑level analysts (0‑2 years) earn $70k–$90k annually. Mid‑level analysts (3‑5 years) earn $90k–$120k. Senior analysts (6‑8 years) earn $120k–$150k. Lead analysts (9‑12 years) earn $150k–$180k. Managers and directors command $180k–$220k, depending on company size and region.
Which skills and certifications are required for these positions?
Core technical skills include SQL, Python, R, Excel, Tableau, Power BI, and statistical modeling. Key certifications are Google Data Analytics Professional Certificate, Tableau Desktop Specialist, Microsoft Certified: Data Analyst Associate, SAS Certified Data Scientist, CBAP, and PMI‑PBA.
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Over 80% of Find Out More About roles support remote or hybrid arrangements. Companies provide secure VPNs, collaboration tools like Slack, Teams, and Zoom, and enforce data‑privacy protocols to enable off‑site analysts to access dashboards and datasets securely.
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Typical progression starts with Analyst → Senior Analyst → Lead Analyst → Manager → Director → VP of Analytics. Advancement depends on mastering advanced analytics, leading cross‑functional projects, and demonstrating business impact.
What are the current industry trends affecting Find Out More About professionals?
Current trends include AI‑driven predictive analytics, real‑time data dashboards, data democratization through no‑code tools, heightened data‑governance standards, and embedding machine‑learning models directly into product features.

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