• Experience: 5+ years in a data engineering tech lead role, with a proven track record of leading and mentoring a team
• Technical Proficiency: Proven experience in building and scaling data pipelines, data warehouses/lakes, and data models in a cloud environment
• Data Governance: Deep understanding of data governance principles and experience implementing best practices
• Leadership: Strong leadership and communication skills, with the ability to motivate and inspire a team, as well as clearly communicate technical concepts to non-technical stakeholders
• Passion: A passion for building high-quality, scalable data solutions and a relentless focus on enabling data-driven decision making across the organization
• Event-Driven Architecture: Experience with event-driven data architectures and creating cross-functional data models to support various business needs

Desirable

• We are seeking a talented and experienced Data Engineer Lead to join our growing team and play a pivotal role in shaping the future of our data infrastructure
• Strategic Data Solutions: Your work in architecting and developing scalable data solutions will empower the entire organization to make data-driven decisions, leading to better business outcomes and strategic growth
• Customer Success Enablement: By creating robust data models and ensuring data accuracy, you will directly contribute to improving the customer experience on the Kajabi platform, helping our users achieve their business goals
• Driving Innovation: Your leadership in adopting cutting-edge data engineering practices and technologies will keep Kajabi at the forefront of the industry, driving continuous improvement and innovation
• Cross-Functional Collaboration: Collaborate with various teams to understand their data needs, providing insights that support both internal business intelligence and customer-facing analytics, ultimately influencing key business decisions
• Role Expectations:
• Technical Leadership: Allocate approximately 60% of your time to hands-on technical work, including data pipeline development, data modeling, and ensuring data quality. The remaining 40% will focus on strategic planning, team leadership, and cross-functional collaboration
• Collaborative Development: Engage with product, engineering, and business stakeholders to gather requirements, provide data insights, and ensure alignment on project goals
• Provide strategic vision and direction: Collaborate with stakeholders across the organization to define a long-term data strategy that aligns with business goals and drives data-driven decision making
• Focus on creating cross-functional data models: Enable self-serve analytics and move towards a semantic model in dbt
• Lead by example: Architect and develop complex data pipelines, data models, and data solutions, ensuring scalability, reliability, and efficiency
• Empower the data team: Foster a collaborative and high-performing data engineering team by providing mentorship, guidance, and opportunities for professional development
• Champion data quality: Implement data governance best practices and lead initiatives to ensure data accuracy, consistency, and completeness across the organization
• Bridge the gap: Collaborate with product, engineering, data science, and business stakeholders to understand data needs and translate them into actionable technical solutions
• Stay ahead of the curve: Stay up-to-date on the latest data engineering trends and technologies and champion their adoption to continuously improve our data infrastructure