Data Engineering: You will guide teams by building optimal data artifacts (including datasets and visualizations) to address key questions. You will refine our systems, design logging solutions, and create scalable data models. Ensuring data security and quality, and with a strong focus on efficiency, you will suggest architecture and development approaches and data management standards to address complex analytical problems. Product leadership: You will use data to shape product development, identify new opportunities, and tackle upcoming challenges. You\'ll ensure our products add value for users and businesses, by prioritizing projects, and driving innovative solutions to respond to challenges or opportunities. Communication and influence: You won\'t simply present data, but tell data-driven stories. You will convince and influence your partners using clear insights and recommendations. You will build credibility through structure and clarity, and be a trusted strategic partner.

Responsibilities

Conceptualize and own the data architecture for multiple large-scale projects, while evaluating design and operational cost-benefit tradeoffs within systems
• Create and contribute to frameworks that improve the efficacy of logging data, while working with data infrastructure to triage issues and resolve
• Collaborate with engineers, product managers, and data scientists to understand data needs, representing key data insights visually in a meaningful way
• Define and manage Service Level Agreements for all data sets in allocated areas of ownership
• Determine and implement the security model based on privacy requirements, confirm safeguards are followed, address data quality issues, and evolve governance processes within allocated areas of ownership
• Design, build, and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains
• Solve our most challenging data integration problems, utilizing optimal Extract, Transform, Load (ETL) patterns, frameworks, query techniques, sourcing from structured and unstructured data sources
• Assist in owning existing processes running in production, optimizing complex code through advanced algorithmic concepts
• Optimize pipelines, dashboards, frameworks, and systems to facilitate easier development of data artifacts
• Influence product and cross-functional teams to identify data opportunities to drive impact
• Mentor team members by giving/receiving actionable feedback

Minimum Qualifications
• Bachelor\'s degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 7+ years of experience where the primary responsibility involves working with data. This could include roles such as data analyst, data scientist, data engineer, or similar positions
• 7+ years of experience with SQL, ETL, data modeling, and at least one programming language (e.g., Python, C++, C#, Scala or others.)
• Experience designing and implementing privacy or data-governance controls in large-scale production data pipelines, including consent or eligibility logic, personal-information filtering or anonymization, and data deletion and retention requirements
• Experience implementing access controls and data-governance mechanisms for sensitive data, including automated or programmatic enforcement rather than manual remediation
• Experience translating privacy, legal, or regulatory requirements into verifiable technical controls, working with policy, legal, or compliance partners

Preferred Qualifications
• Master\'s or Ph.D degree in a STEM field
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience building privacy or compliance controls for data used in machine learning or model training
• Experience establishing data-governance standards and onboarding processes that multiple teams or product surfaces adopt
• Experience implementing data deletion propagation or retroactive data-remediation processes across historical data
• Experience serving as a technical lead, including setting technical direction and mentoring other engineers