Job Summary We are hiring a Lead Data Engineer to drive delivery across our analytics and data platform initiatives. This role is a hands-on technical lead responsible for translating well-defined engineering specifications into executable work, coordinating a small team of contractors, and ensuring consistent, high-quality progress. You will partner closely with an Engineering Manager who owns architectural direction and high-level specifications. Your focus will be execution leadership: refining specifications into agile workloads, assigning work intelligently, contributing directly to complex implementations, and clearly communicating progress, risks, and clarity needs. What You'll Do: - Lead a small delivery team and act as a player-coach by owning the most complex data transformations and modeling work, while reviewing and guiding contractor contributions. - Keep work unblocked, well-scoped, and on track to ensure consistent delivery of momentum. - Continue building and refining analytics foundations driven by Gold-layer data models. - Design and implement Medallion architecture transformations (Bronze → Silver → Gold). - Ensure Gold-layer outputs are well-modeled, performant, and consistently consumable by an external analytics engine. - Improve the reliability, clarity, and maintainability of existing pipelines. - Partner with the Engineering Manager to execute an approved Data Warehouse Unification Plan RFC. - Plan and deliver sprint-based execution, with execution as the primary focus given the RFC is already defined. - Work across multiple Databricks instances as part of a GDPR-first data strategy. - Surface execution risks, sequencing concerns, and dependencies early to support successful delivery. - Translate engineering specifications into clear, scoped tickets, logical sprint plans, and well-sequenced workstreams. - Assign work appropriately across contractors based on skill level and complexity. - Implement complex Spark and Databricks transformations directly as needed. - Review pull requests and uphold quality standards across all delivered work. - Communicate progress, blockers, and clarity needs clearly and proactively with stakeholders. - Maintain delivery momentum without unnecessary process overhead. What We Need: - Strong hands-on experience with Databricks, PySpark, and Spark SQL - Proven experience implementing Medallion architecture - Solid data modeling skills, especially for analytics and reporting use cases - Experience leading delivery for small engineering teams (formal management not required) - Comfort working with partially defined requirements and refining them collaboratively - Clear written and verbal communication skills in a remote environment Nice to Have: - Experience supporting GDPR-compliant data strategies - Exposure to multi-region or multi-tenant data platforms - Familiarity with external analytics tools consuming warehouse data - Familiarity with various data flows, with sources in Azure, AWS, GCP, and others Why Logicbroker: Mission-Driven Culture: Be part of a company transforming digital commerce through innovation and agility—your work directly shapes how global brands connect with customers. Collaborative, No-Ego Environment: We believe the best ideas win, not the loudest voices. You’ll work alongside teammates who challenge and support each other. Hybrid Flexibility with High-Performance Energy: Whether remote or in-office, we foster autonomy and accountability—because we trust you to own your success. Leadership That Listens: Our executives are not just accessible—they’re invested in your growth, open to your ideas, and committed to building a company where people thrive. Celebrated Wins, Shared Learnings: From team offsites to Slack shoutouts, we celebrate progress and learn from setbacks together

• Lead the Member Foundations Data Engineering team • Build the core datasets that represent how members interact with Netflix product • Influence and guide projects from end-to-end: ideation to production • Ensure data products are built with engineering rigor and strong data quality guarantees • Invest in tooling and best practices for achieving goals

• Hire, coach, and grow a diverse, high-performing team of data and software engineers • Develop and execute a clear, impact-oriented roadmap for the team • Oversee the design, building, and scaling of robust, well-modeled, and reliable data products • Lead a function dedicated to building reusable data frameworks, development tooling, and automation capabilities • Define and drive best practices for data modeling, pipeline architecture, testing, and observability • Act as the primary technical partner to the central Data Platform team • Build strong relationships with senior stakeholders across various departments • Provide clear direction and priority alignment in a fast-paced environment

• Develop, maintain, and monitor data ingestion and enrich ETL/ELT pipelines within the platform that load and convert raw data into data products. • Partner with regional and/or global IT infrastructure teams to support and configure data platform storage and compute layers. • Maintain and build CI/CD pipeline code and automated test plans to ensure automated deployment between development and production environments. • Manage data platforms related to ITSM ticketing processes (incident & change requests). • Collaborate with data team members, architects, data stewards, data owners, and business SMEs to develop data product business requirements for data cleansing and enrichment. • Implement data security and data governance policies within the data platform to protect sensitive information and maintain data quality. • Partner with cybersecurity and compliance teams to rigorously ensure compliance with applicable data security, data protection, and regulation requirements. • Partner with global data teams to ensure that the local data platform & products maintain interoperability. • Continuously identify and drive opportunities to improve platform performance, reduce complexity and technical debt and reduce cloud computing and storage costs. • Maintain support documentation within the team repository. • Escalate data platform issues to the attention of management / appropriate partners. • Leverage agile frameworks and Azure DevOps to execute the team backlog. • Implement data platform metadata management standards and policies.