Product Manager owning Spotify’s data catalogue, metadata, and lineage roadmap. Improving how engineers, analysts, and AI agents discover and safely use data.
About the role
- Staff Data Engineer building scalable data platforms for Penn Entertainment’s gaming, sports betting, and media products. Leading architecture, data quality, analytics engineering, and technical mentorship.
Responsibilities
- Collaborate with business stakeholders to identify and prioritize data-driven opportunities and initiatives
- Design, build, and maintain scalable data pipelines supporting analytics, reporting, product, and operational use cases
- Develop, test, and optimize dbt models following data modeling best practices
- Manage and support orchestration platforms including Airflow, with exposure to Dagster
- Design and implement dimensional, semantic, and analytical data models for self-service analytics and business intelligence
- Drive data architecture decisions and establish engineering standards and best practices
- Partner with data engineers and platform teams to improve data ecosystem reliability, scalability, and performance
- Develop and maintain data quality frameworks, testing strategies, and observability solutions
- Support governance through documentation, metadata management, lineage, and data ownership practices
- Complete complex analyses and provide actionable recommendations to business stakeholders
- Oversee team analysis for data accuracy, consistency, and business relevance
- Champion adoption of data exploration and self-service analytics tools
- Lead technical design reviews and mentor through code reviews, architectural guidance, and knowledge sharing
- Support data engineer hiring, onboarding, mentoring, and professional development
- Participate in incident response, root cause analysis, and continuous improvement for critical data platform issues
- Perform other duties as required
Requirements
- Bachelor's degree in Computer Science, Data Engineering, Statistics, Information Systems, Business, or a related field
- 8+ years of experience in data engineering, analytics engineering, or a related discipline
- Hands-on expertise with BigQuery, dbt, Airflow, Python, SQL, and modern cloud data platforms
- Strong understanding of data warehousing concepts, ETL/ELT design patterns, and dimensional modeling
- Experience building and optimizing scalable data transformation pipelines in cloud environments
- Experience with Google Cloud Platform services such as BigQuery, Cloud Storage, Pub/Sub, and Cloud Composer
- Exposure to Azure data platforms and services is a plus
- Proficiency with Git-based development workflows, CI/CD pipelines, and software engineering best practices
- Experience implementing automated testing, monitoring, and data observability solutions
- Experience with data visualization and exploration tools such as Looker, Mode, Tableau, or similar platforms
- Excellent communication and stakeholder management skills, with the ability to translate business requirements into scalable technical solutions
- Proven ability to lead complex projects and influence technical direction across multiple teams
Benefits
- Competitive compensation package
- Fun, relaxed work environment
- Education and conference reimbursements
- Opportunities for career progression and mentoring others
- Bonus eligibility for most non-sales positions
- Best-in-class benefits with options and support for physical, financial, and emotional wellbeing
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