Manager, Data Engineering

Overview

This is a hybrid role - 2 days remote and 3 days in the Malvern, PA office. Cube. Smart is seeking a Manager, Data Engineering to lead the team responsible for building and evolving the enterprise data platform that powers analytics, reporting, AI, and critical business decision-making across the organization. This role combines strategic leadership with hands-on technical expertise.

The ideal candidate is an experienced people leader who has successfully designed, delivered, and operated modern cloud data platforms and lakehouse architectures. You will partner closely with business and technology leaders to deliver trusted data products, enable self-service analytics, and expand data and AI capabilities that help Cube.

Smart unlock greater value from its data. Who we are: At Cube. Smart, we’re intentional about culture. You can experience it everywhere from our mission statement of “genuine care” to our “It’s What’s Inside That Counts” tagline to calling each other “teammates” rather than employees. This spirit fosters a fun and collaborative environment that has resulted in our rapid growth and being recognized amongst the top in our industry.

Cube. Smart’s award-winning team is made up of people who genuinely care. Teammates care about our customers and the life events and/or business needs they are facing. Teammates are passionate, responsible and understanding. The Cube. Smart team is made up of people who have a can-do attitude, are committed to their own success and the success of the company, and lead by example.

If this sounds like a team and culture that matches your personal values and motivations, we want to hear from you.

Responsibilities

Lead, coach, and develop a high-performing Data Engineering team through hiring, mentoring, career development, and performance management. Advance strategy, roadmap, and evolution of Cube. Smart’s cloud data platform, enabling Analytics, Reporting, Data Science, Machine Learning, AI, and other enterprise data capabilities.

Drive adoption of the enterprise data platform through self-service analytics, trusted data products, conversational analytics, and AI agents that enable faster, data-informed decision-making. Evaluate emerging cloud, data, and AI technologies and identify opportunities to modernize, automate, and optimize enterprise capabilities.

Foster a product-oriented engineering culture focused on delivering reusable, scalable, and business-aligned data products. Provide hands-on technical leadership across the data lifecycle, including architecture, modeling, ingestion, integration, batch and streaming pipelines, data quality, metadata and lineage, governance, and delivery of trusted data products.

Define and implement platform controls for security, access, auditability, disaster recovery, resiliency, compliance, and operational risk Partner with IT leadership and strategic vendors on platform optimization, cost management, and technology investments. Communicate architecture decisions, technical tradeoffs, risks, and business impact effectively to both technical and non-technical audiences.

Qualifications

Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field. 7+ years of experience in data engineering, data platforms, or related technology disciplines, including experience leading engineering teams. Proven ability to develop talent, build accountability, and foster a collaborative, high-performing culture.

Strong hands-on experience with SQL, Python, Py. Spark, and modern data engineering practices. Experience designing, building, and operating modern cloud data platforms and lakehouse architectures, with hands-on experience in Databricks. Experience delivering Databricks-based data engineering solutions, including data ingestion, transformation, orchestration, governance, and production operations.

Strong understanding of data architecture, modeling, distributed data processing, data quality, security, governance, scalability, and production operations. Experience enabling AI, Machine Learning, Analytics, and Reporting workloads through enterprise data platforms, with an understanding of the data and engineering capabilities required to support AI-driven solutions.

Experience leading teams using Agile delivery practices in a fast-paced environment. Excellent communication, problem-solving, and decision-making skills, with the ability to translate complex technical concepts into business value.

Preferred Qualifications

Experience with the Databricks Lakehouse Platform, including Delta Lake, Unity Catalog, AI agents, Apps, and related platform capabilities. Experience enabling modern AI and analytics use cases, including conversational analytics, semantic-layer experiences, AI agents, and business-facing data applications. Experience with Microsoft Azure data services, including Azure Data Factory and Azure SQL.

Experience implementing CI/CD, Data. Ops, and DevOps practices for data platforms and products. Experience with cloud cost optimization and Fin. Ops practices. Understanding of Machine Learning lifecycle management and AI-enabled analytics. Master’s degree and/or relevant cloud, data, AI, or Databricks certifications.

We are an Equal Opportunity Employer, Minority/Female/Veteran/Individuals with Disabilities/Sexual Orientation/Gender Identity. #LI-MT1 #LI-Hybrid Lead, coach, and develop a high-performing Data Engineering team through hiring, mentoring, career development, and performance management. Advance strategy, roadmap, and evolution of Cube.

Smart’s cloud data platform, enabling Analytics, Reporting, Data Science, Machine Learning, AI, and other enterprise data capabilities. Drive adoption of the enterprise data platform through self-service analytics, trusted data products, conversational analytics, and AI agents that enable faster, data-informed decision-making.

Evaluate emerging cloud, data, and AI technologies and identify opportunities to modernize, automate, and optimize enterprise capabilities. Foster a product-oriented engineering culture focused on delivering reusable, scalable, and business-aligned data products. Provide hands-on technical leadership across the data lifecycle, including architecture, modeling, ingestion, integration, batch and streaming pipelines, data quality, metadata and lineage, governance, and delivery of trusted data products.

Define and implement platform controls for security, access, auditability, disaster recovery, resiliency, compliance, and operational risk Partner with IT leadership and strategic vendors on platform optimization, cost management, and technology investments. Communicate architecture decisions, technical tradeoffs, risks, and business impact effectively to both technical and non-technical audiences.

Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field. 7+ years of experience in data engineering, data platforms, or related technology disciplines, including experience leading engineering teams. Proven ability to develop talent, build accountability, and foster a collaborative, high-performing culture.

Strong hands-on experience with SQL, Python, Py. Spark, and modern data engineering practices. Experience designing, building, and operating modern cloud data platforms and lakehouse architectures, with hands-on experience in Databricks. Experience delivering Databricks-based data engineering solutions, including data ingestion, transformation, orchestration, governance, and production operations.

Strong understanding of data architecture, modeling, distributed data processing, data quality, security, governance, scalability, and production operations. Experience enabling AI, Machine Learning, Analytics, and Reporting workloads through enterprise data platforms, with an understanding of the data and engineering capabilities required to support AI-driven solutions.

Experience leading teams using Agile delivery practices in a fast-paced environment. Excellent communication, problem-solving, and decision-making skills, with the ability to translate complex technical concepts into business value.

Preferred Qualifications

Experience with the Databricks Lakehouse Platform, including Delta Lake, Unity Catalog, AI agents, Apps, and related platform capabilities. Experience enabling modern AI and analytics use cases, including conversational analytics, semantic-layer experiences, AI agents, and business-facing data applications. Experience with Microsoft Azure data services, including Azure Data Factory and Azure SQL.

Experience implementing CI/CD, Data. Ops, and DevOps practices for data platforms and products. Experience with cloud cost optimization and Fin. Ops practices. Understanding of Machine Learning lifecycle management and AI-enabled analytics. Master’s degree and/or relevant cloud, data, AI, or Databricks certifications.

We are an Equal Opportunity Employer, Minority/Female/Veteran/Individuals with Disabilities/Sexual Orientation/Gender Identity. #LI-MT1 #LI-Hybrid