Specialist - Cloud Engineering

Role Senior Analytics Engineer About the Role We are seeking a Senior Data Engineer specializing in SQL and dbt to build and maintain curated analytics datasets derived from highvolume playback content platform partner and qualityofservice data This is a hands-on engineering role that also requires direct stakeholder ownership The contractor will independently gather requirements clarify ambiguous requests negotiate scope and timelines communicate risks and respond to urgent stakeholder needs while maintaining data quality and engineering standards This is not primarily a dashboarddevelopment role or a position where stakeholder communication is handled entirely by a project manager The successful candidate must be comfortable owning both the technical work and the relationship with the people requesting it Key Responsibilities Design build test document and maintain production dbt models Write and optimize complex SQL over highvolume event playback subscriber content and partner datasets Develop scalable incremental models transformations aggregations and backfill strategies Define and maintain clear model grains metric definitions lineage dependencies and data contracts Implement dbt tests and other validation controls to protect data quality Investigate data discrepancies and explain findings to both technical and non-technical stakeholders Evaluate query performance processing cost model materialization join strategy and pipeline runtime Troubleshoot production pipeline failures and safely coordinate fixes reruns and backfills Own assigned work from initial stakeholder request through requirements implementation validation release and follow-up Convert incomplete or ambiguous requests into clearly scoped deliverables and acceptance criteria Triage urgent requests based on business impact technical risk and existing priorities Communicate delivery options tradeoffs risks and realistic completion dates Provide proactive status updates and escalate risks before they become delivery surprises Collaborate with analytics content strategy reporting product data science and upstream dataengineering teams Participate in code reviews and contribute to team standards reusable patterns and technical documentation Maintain reliable workinghour overlap with USbased stakeholders and team members Required Qualifications Typically five or more years of experience in data engineering analytics engineering business intelligence engineering or a comparable datafocused role Expert SQL skills including complex transformations window functions large joins dimensional modeling performance optimization and data validation Substantial recent hands-on experience using dbt in a production environment Ability to design and build dbt models from scratch rather than only executing or maintaining models created by others Strong knowledge of dbt model organization sources references tests documentation Jinja macros incremental models dependencies and deployment practices Experience developing efficient transformations over largevolume datasets in a cloud data warehouse or lakehouse Experience owning production data pipelines including troubleshooting validation releases reruns and historical backfills Strong understanding of data modeling model grain metric consistency lineage and dataquality practices Experience using Git pull requests code review and CICDbased development processes Professional working proficiency in spoken and written English Availability to participate in meetings and stakeholder conversations during agreedupon US business hours Required Stakeholder Experience Candidates must have prior experience directly owning relationships with business product analytics reporting or other data consumers This experience must include Leading requirements and scoping conversations without relying on a manager or project manager as the primary intermediary Clarifying vague requests and identifying the business decision or outcome behind them Translating business questions into data requirements and implementation plans Negotiating scope priority timelines and technical tradeoffs Managing competing requests from multiple stakeholders Responding constructively to urgent or highpressure requests Communicating delays