Lead Data Engineer Location: Fort Washington United States Job Description: Piper Companies is seeking a Lead Data Engineer to support a company focused on enterprise digital transformation and advanced cloud‑based data modernization initiatives. This position is hybrid in Ft. Washington, PA. The Sr Data Engineer will provide strategic direction for enterprise‑wide data architecture and cloud infrastructure. This role enables the organization to leverage data as a competitive advantage by driving innovation, adopting emerging technologies, and shaping long‑term architectural vision. Responsibilities for the Lead Data Engineer include: - Defining enterprise‑level data architecture and infrastructure strategies - Designing advanced data warehouses, data lakes, and hybrid cloud architectures - Driving adoption of automation, DevOps, and CI/CD best practices for data platforms - Partnering with executive leadership to align data infrastructure with business strategy - Guiding cloud resource planning, cost optimization, and capacity management Required Qualifications for the Lead Data Engineer include: - 7+ years of experience in data engineering, cloud data ecosystems, and infrastructure strategy - Expertise with cloud‑based data tools such as Azure Fabric, Data Factory, Synapse, or similar - Proficiency in Python, SQL, ETL development, and data integration architecture - Strong understanding of Azure security controls, resource management, and governance frameworks - Bachelor's Degree in Business, Computer Science, Finance, Information Systems, or related field Compensation for the Lead Data Engineer includes: Salary Range: $140,000-$150,000 depending on experience Full Benefits Package: PTO, Paid Holidays, Medical, Dental, Vision, 401K, Tuition Reimbursement, Sick leave as required by law #LI-SM2 #LI-HYBRID SEO Keywords Enterprise Data Architect, Cloud Infrastructure Architect, Azure Data Lead, Data Warehouse Architect, Data Lake Architect, Azure Synapse, Azure Fabric, Data Governance Lead, Cloud Strategy Architect, DevOps for Data, CI/CD Data Engineering, Python Data Engineer, Cloud Security Governance, Enterprise Architecture, Data Modernization

• Lead and develop Fusion’s Customer Excellence data practice. • Define and evolve service offerings covering commercial data platforms, CRM ecosystems, and customer master data. • Build and mentor a global team of data and CRM consultants. • Lead customer data and CRM transformation programs across global life sciences organizations. • Support initiatives involving Veeva CRM, Veeva Network, customer master data management, and related commercial platforms. • Ensure solutions align with data governance, compliance, and regulatory requirements. • Define target operating models for customer data and commercial platforms. • Design scalable data governance frameworks and master data lifecycle processes. • Align commercial business needs with technology platforms and data architecture. • Manage and coordinate global project teams across multiple client engagements. • Support capability development and recruitment within the Customer Excellence practice. • Coach consultants and promote best practices across delivery teams. • Contribute to client proposals, solution design, and strategic pitches. • Identify opportunities to expand commercial data and customer excellence programs. • Support the growth of Fusion’s Data & Commercial Platforms portfolio.

• Design, build, and maintain scalable data pipelines and solutions on Microsoft Azure or similar cloud platforms. • Develop and optimize ETL/ELT workflows to support high-volume, high-velocity data ingestion. • Implement robust data models and structures that support analytics, reporting, and machine learning workloads. • Integrate new data sources—internal and external—into the enterprise data ecosystem to expand data availability and unlock new business insights. • Partner with product, engineering, business and global teams to identify opportunities for new datasets and ensure seamless onboarding. • Establish scalable frameworks for data discovery, cataloging, and lineage to support enterprise wide data growth. • Automate data workflows, quality checks, and monitoring using Cloud native tools and Databricks capabilities. • Collaborate with data scientists to operationalize AI models using Databricks, Azure Machine Learning, or similar platforms. • Ensure data readiness, reliability, and accessibility to accelerate AI adoption and experimentation. • Contribute to the development of an enterprise AI strategy by identifying data gaps, opportunities, and scalable patterns. • Work closely with cross functional teams to translate business requirements into scalable data solutions. • Provide technical guidance and best practices on Azure and Databricks to engineering and analytics teams. • Participate in code reviews, architecture discussions, and continuous improvement initiatives. • Assist in identifying & defining new systems functionality within the Go To Market technology stack. • Improve processes/workflows within the Sales organization in regard to Sales applications and system support. • User support - troubleshooting, identifying problems and working with Local & Global IT to resolve technical issues and work with users to provide proper training. • Assist in the analysis of underlying system issues arising from investigations into requirements and problems, and identify available solutions for consideration.

• Own end-to-end data and AI solution architecture across modern cloud data platforms • Design and implement enterprise data architectures across SOO → SOR → CDM (Curated) layers • Lead core Data Architecture disciplines, including: - conceptual, logical, and physical data modeling; MDM and reference data; metadata, lineage, and governance • Design and build scalable batch and real-time data pipelines using lakehouse and warehouse patterns • Enable and support AI / ML use cases on data platforms, including feature engineering, model integration, orchestration, and monitoring • Provide architectural leadership on production-grade systems, covering CI/CD, security, performance, and observability • Partner with engineers, delivery teams, and client stakeholders to align on architectural tradeoffs and delivery decisions • Act as a proactive thought partner, identifying opportunities for simplification, acceleration, and AI-driven value creation