Senior Engineering Insights Analyst - Power BI / SQL / Databricks / Semiconductor Industry

Requirements

• Required Qualifications
• Bachelor's degree in Computer Science, Engineering, Data Science, Business Analytics, or related field.
• 5+ years in engineering analytics, BI development, or program reporting (semiconductor/hardware preferred).
• Strong proficiency with Power BI (or equivalent) and dashboard design for executive audiences.
• Strong hands-on experience with Jira (JQL, boards, dashboards) and Agile metrics.
• Strong SQL skills for data extraction/transformation and building trusted datasets.
• Experience integrating multiple data sources (BI + "system of record" tools) and working with data owners/IT.
• Ability to define KPIs and communicate insights clearly to senior leadership and non-technical stakeholders.
• Familiarity with product/AI metrics (adoption, cost, performance, quality) and how to operationalize them in reporting.
• Python (or similar) for data processing/automation.
• Experience with Databricks (or equivalent data platform) and multi-site/global reporting.
• Experience with capacity planning/resource tools (e.g., P6) and cost modeling.
• Exposure to AI/LLM telemetry/observability signals (usage, latency, cost, quality/evals) and integrating them into dashboards.
• Experience building metric definitions/scorecards used across multiple teams (governance + adoption).
• Expected Base Pay Range (USD)
• 111,070 - 166,400, $ per annum
• The successful candidate's starting base pay will be determined based on job-related skills, experience, qualifications, work location and market conditions. The expected base pay range for this role may be modified based on market conditions.
• Additional Compensation and Benefit Elements
• All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status.
• Any applicant who requires a reasonable accommodation during the selection process should contact Marvell HR Helpdesk at TAOps@marvell.com .

Benefits

Additional Information

About Marvell Marvell's semiconductor solutions are the essential building blocks of the data infrastructure that connects our world. Across enterprise, cloud and AI, and carrier architectures, our innovative technology is enabling new possibilities. At Marvell, you can affect the arc of individual lives, lift the trajectory of entire industries, and fuel the transformative potential of tomorrow. For those looking to make their mark on purposeful and enduring innovation, above and beyond fleeting trends, Marvell is a place to thrive, learn, and lead. Your Team, Your Impact The MBE AI Strategy & Enablement team drives engineering productivity and operational excellence across Marvell's Software & Firmware Division. We also define and track the metrics that matter - from developer efficiency to lifecycle impact - providing leadership with the insights needed to make high-confidence decisions at scale. Our work touches every major engineering function, giving you a front-row seat to how data shapes strategy across a global organization. What You Can Expect Build and maintain executive- and team-level dashboards using Power BI , Jira dashboards , and other internal reporting systems. Develop sprint and delivery dashboards (velocity, burn-down, throughput/flow, scope change, defect trends, predictability). Create org-level dashboards for headcount/capacity, cost trends, and forecasting (partnering with Finance/Resource Planning). Build program tracking dashboards for key initiatives (milestones, status, risks, dependencies, and delivery health). Build and maintain AI initiative dashboards (PoC → pilot → production) including adoption, usage, cost, latency, quality, and guardrail/incident signals. Define and standardize KPI definitions across teams; ensure consistency in how metrics are calculated and reported. Integrate data from Jira and other internal systems (e.g., planning tools, internal data platforms, engineering systems) into reliable BI datasets. Identify trends/anomalies and provide concise executive readouts with recommended actions. Improve data quality (validation checks, reconciliations, source-of-truth alignment) and reduce manual reporting. Maintain lightweight documentation for dashboards (data sources, logic, definitions, ownership).

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