Senior Content Engineer

Flexera saves customers billions of dollars in wasted technology spend. A pioneer in Hybrid ITAM and FinOps, Flexera provides award-winning, data-oriented SaaS solutions for technology value optimization (TVO), enabling IT, finance, procurement and cloud teams to gain deep insights into cost optimization, compliance and risks for each business service. Flexera One solutions are built on a set of definitive customer, supplier and industry data, powered by our Technology Intelligence Platform, that enables organizations to visualize their Enterprise Technology Blueprint™ in hybrid environments—from on-premises to SaaS to containers to cloud.

We’re transforming the software industry.  We’re Flexera.  With more than 50,000 customers across the world, we’re achieving that goal. But we know we can’t do any of that without our team.  Ready to help us re-imagine the industry during a time of substantial growth and ambitious plans?  Come and see why we’re consistently recognized by Gartner, Forrester and IDC as a category leader in the marketplace. Learn more at flexera.com

We are looking for a Senior Content Data Engineer to join our Content Operations team and play a key role in maintaining, analyzing, and improving the quality of technology product data.

The role combines data analysis, content quality, Power BI reporting, AI-driven process improvement, and technology domain expertise. The analyst will work with large and complex datasets to identify data gaps and inconsistencies, support normalization and catalog curation, build meaningful insights through dashboards, and identify opportunities to use AI and automation to improve operational efficiency and data quality.

The ideal candidate should have strong analytical and problem-solving skills, experience with data visualization and reporting, curiosity about AI and automation, and the ability to work closely with Engineering, Product Management, and Operations teams.

Key Responsibilities

• Design, develop, and maintain Power BI dashboards and reports to provide visibility into content quality, operational performance, backlog, SLA, automation coverage, and data trends.
• Build meaningful KPIs and visualizations to help stakeholders monitor data quality and operational health.
• Analyze data from multiple sources and create dashboards that provide actionable insights to Operations, Product, and Engineering teams.
• Develop automated reporting solutions to reduce manual reporting effort.
• Continuously improve existing dashboards based on business requirements and stakeholder feedback.
• Use Power BI to identify trends, bottlenecks, quality patterns, and opportunities for process improvement.
• Identify opportunities to leverage AI, machine learning, and intelligent automation to improve content operations and data quality.
• Explore and implement AI-driven solutions for data classification, normalization, mapping recommendations, anomaly detection, and quality validation.
• Develop and evaluate AI-assisted approaches for identifying inconsistent mappings, missing content, duplicate products, and potential data-quality issues.
• Analyze large datasets to identify data gaps, inconsistencies, anomalies, duplicates, and quality issues.
• Perform and support data normalization, mapping, classification, enrichment, and validation.
• Review technology product, manufacturer, version, release, and lifecycle data to ensure accuracy, completeness, and consistency.

Required Skills & Qualifications

• Bachelor's degree in Computer Science, Information Technology, Data Analytics, Engineering, or a related field.
• 2–5 years of experience in data analysis, data quality, content operations, data management, or a related role.
• Strong analytical and problem-solving skills with experience working with large datasets.
• Strong proficiency in Excel and Power BI.
• Working knowledge of SQL, python for data analysis and investigation.
• Understanding of data quality, normalization, mapping, classification, and validation concepts.
• Strong attention to detail and commitment to data accuracy.
• Good understanding of data visualization and KPI development.
• Basic understanding of AI/ML concepts and their application in data analysis and automation.
• Strong communication and stakeholder-management skills.
• Ability to manage multiple priorities and work effectively in a fast-paced environment.

Good to Have

• Experience working with IT asset management (ITAM), technology/product catalogs, CMDB, or IT service management (ITSM) data.
• Experience with Power BI or other data visualization and reporting tools.
• Experience with Python or other scripting languages for data analysis and automation.
• Knowledge of APIs, data pipelines, web crawlers, or integrations.
• Familiarity with JIRA or similar ticket-management tools.
• Experience working with product lifecycle data such as GA, EOL, EOS, and obsolete products.
• Experience with data governance, data quality frameworks, or master data management.
• Exposure to AI/ML-based data automation or rule-generation solutions.

Key Competencies

• Analytical Thinking: Ability to break down complex data problems and identify root causes.
• Data Quality Mindset: Strong focus on accuracy, consistency, completeness, and reliability of data.
• Problem Solving: Ability to investigate issues and develop practical solutions.
• Domain Expertise: Ability to understand technology products and build expertise in the assigned data domain.
• Ownership: Takes responsibility for assigned work and drives issues through to resolution.
• Collaboration: Works effectively with Engineering, Product, Operations, and other stakeholders.
• Continuous Improvement: Looks for opportunities to improve processes, automation, and operational efficiency.
• Attention to Detail: Able to identify subtle inconsistencies and maintain high-quality data standards.

The Following Personal Qualities Are Desired

• Continuous Learner: Demonstrates a strong passion for self-learning, stays curious about emerging technologies, and actively engages with local or online technology communities.
• Analytical & Detail-Oriented: Highly motivated with strong attention to detail, analytical thinking, and problem-solving skills, with a focus on delivering accurate and high-quality results.
• Self-Driven & Accountable: Takes ownership of responsibilities, works independently, and is willing to go the extra mile when the team is working toward critical goals or tight deadlines.
• Confident & Respectful: Communicates with confidence while maintaining professionalism, respect, and a collaborative approach when working with others.
• Strong Communicator: Ability to communicate clearly, effectively, and efficiently within the team and across cross-functional teams and stakeholders.
• Collaborative Mindset: Works effectively as part of a team, shares knowledge, supports colleagues, and contributes positively to a collaborative and inclusive work environment.
• Adaptable & Improvement-Focused: Comfortable working in a fast-paced environment, embraces change, and proactively identifies opportunities to improve processes, quality, and operational efficiency.