Practice Lead - Data

Impower.ai helps Fortune 1000 enterprise organizations solve complex business challenges through modern Data, AI, and engineering solutions. Impower designs and delivers scalable platforms that enable intelligent decision-making, advanced analytics, automation, and AI- driven outcomes. Role Summary The Practice Lead – Data & Analytics is a technical and architectural leader within the Data & Analytics Practice, serving as a thought leader who supports the Practice Director in building and growing the practice. This role owns solution architecture, delivery execution, and senior client advisory for complex data & analytics engagements while contributing to the growth, capabilities, and market positioning of the practice. The Practice Lead is expected to architect and lead the delivery of modern enterprise data platforms for Fortune 1000 clients while also helping build the practice itself—its people, methodologies, reusable assets, offerings, and point of view. What You Will Do Lead Client Solutions Act as a trusted technical advisor to CIOs, CDOs, data leaders, and senior technology stakeholders Lead discovery, architecture, and solution design for enterprise data initiatives Translate complex business requirements into scalable data architectures and Guide clients through decisions involving architecture, platforms, modernization, governance, scalability, and cost Communicate complex technical concepts in a way that business and technology leaders Architect Modern Data Platforms Design and deliver modern cloud data platforms using technologies such as Snowflake, Databricks, dbt, Azure, AWS, and GCP Architect scalable data ingestion, transformation, storage, modeling, and consumption patterns Design modern ELT/ETL pipelines across structured, semi-structured, streaming, and Establish architectures supporting analytics, BI, machine learning, and AI workloads Lead modernization efforts from legacy data warehouses and on-premise platforms to Evaluate technologies based on client requirements rather than defaulting to a single platform or stack Establish engineering standards and best practices for data pipelines, modeling, testing, observability, CI/CD, and DataOps Define approaches for data quality, lineage, governance, security, and performance Ensure platforms are designed for scalability, maintainability, reliability, and cost Help clients improve the maturity of their broader data engineering capabilities Establish reusable architecture patterns and delivery accelerators across engagements Lead Delivery Own the technical quality and execution of complex data engineering engagements Translate architecture into executable roadmaps, workstreams, and engineering priorities Work closely with project and product leadership to ensure teams deliver against agreed business outcomes Identify delivery risks early and work directly with clients and Impower leadership to resolve them Maintain accountability for the technical quality of solutions from initial discovery through production Support the Practice Director in defining Impower's point of view on modern enterprise data architecture and engineering Advise clients on data modernization strategies and technology roadmaps Help organizations understand how their data foundation enables analytics, automation, and AI Contribute to proposals, solution designs, capability narratives, and go-to-market materials Participate in client-facing strategy and discovery conversations beyond technical delivery Lead and Grow the Team Lead, manage, mentor, and develop data engineers and architects assigned to client Provide technical direction while creating accountability for delivery outcomes Develop engineers' architectural, consulting, and client-facing capabilities Participate in recruiting and evaluating technical talent Build a culture of technical excellence, ownership, curiosity, and continuous improvement Support the Practice Director in building reusable capabilities, delivery standards, and intellectual property for the practice Core Technical Expertise / Exposure Snowflake architecture, engineering, optimization, and data modeling Databricks, including Lakehouse architecture, Delta Lake, Spark, and modern data dbt and modern analytics engineering practices Cloud data architecture across Azure, AWS, and/or GCP Modern ETL/ELT and orchestration technologies such as Azure Data Factory, Airflow, Databricks Workflows, or equivalent Data warehouse, data lake, and lakehouse architectures Dimensional, relational, and modern analytical data modeling Batch and streaming data pipelines SQL and Python APIs and enterprise data integration patterns CI/CD and DataOps practices for data engineering Data quality, observability, lineage, metadata management, and governance Performance optimization and cloud cost management Working knowledge of how modern data platforms support AI, machine learning, and Qualifications 10+ years of experience in Data Engineering, Data Architecture, Analytics Engineering, or related disciplines Proven experience architecting and delivering enterprise-scale modern data platforms Deep experience with at least one major modern data platform such as Snowflake or Databricks Strong architectural mindset combined with enough hands-on technical depth to guide and challenge engineering teams Experience leading complex data modernization initiatives Experience leading and developing high-performing technical teams Demonstrated ability to operate as a strategic advisor rather than solely a technical delivery lead Strong consulting skills, including structuring ambiguous business problems, leading client discovery, and navigating competing priorities Excellent communication skills with the ability to operate effectively with both engineers and senior executives Experience estimating, scoping, and shaping complex data engineering engagements What Success Looks Like Modern data platforms that are scalable, reliable, cost-effective, and delivering measurable business value Data engineering engagements that consistently meet client expectations for quality and execution High-performing engineering teams operating with strong technical standards and accountability Trusted advisor relationships with senior client stakeholders Clients with stronger data foundations capable of supporting analytics, automation, machine learning, and AI Reusable architectures, accelerators, standards, and methodologies that improve delivery across the Data & Analytics Practice A visible contribution to the growth, capabilities, and market reputation of Impower.ai's #J-18808-Ljbffr Impower.ai