Role Description We are seeking a Senior Analytics Engineer who combines strong data engineering fundamentals with advanced analytical thinking and business acumen. This role is not just about building pipelines; it’s about designing the analytical foundation that powers product decisions, experimentation, and machine learning initiatives. You will translate complex restaurant operational and customer behavior data into scalable, reliable, and insight-ready data models that drive measurable business impact. You will act as a bridge between engineering, analytics, product, and data science ensuring our data platform supports both robust technical systems and high-quality decision-making. What You’ll Own - Business Analytics & Decision Enablement - Translate ambiguous business questions into structured analytical frameworks - Develop KPI definitions, metric layers, and standardized reporting logic across teams - Partner with Product and Growth teams to define success metrics and measurement strategies - Deliver deep-dive analyses on ordering behavior, churn risk, marketplace performance, and operational efficiency - Present insights and trade-offs clearly to senior stakeholders and leadership - Experimentation & Advanced Analytics - Own and scale A/B testing infrastructure, including experiment tracking and evaluation pipelines - Define best practices for hypothesis testing, power analysis, sequential testing, and causal inference - Build experimentation dashboards and automated reporting systems - Partner with Data Science to productionize predictive models and design reusable ML-ready datasets - Support feature engineering and monitoring for production ML systems - Analytics Engineering & Strategic Technical Leadership - Design and maintain scalable ELT/ETL workflows that power analytics, experimentation, and ML use cases - Build and optimize dimensional data models (star schemas, data vault, etc.) to enable self-service analytics - Create trusted, well-documented datasets for product, finance, operations, and growth teams - Improve data quality, observability, lineage, and governance across the analytics ecosystem - Optimize performance across warehouse and transformation layers - Drive best practices in analytics engineering across the organization - Influence tooling decisions (Airflow, dbt, Snowflake, Spark, etc.) with scalability and business usability in mind - Mentor analytics engineers and elevate modeling and measurement standards - Champion a data-driven culture grounded in experimentation and measurable impact Qualifications - 7+ years of experience in analytics engineering, data engineering, or advanced analytics roles - Deep expertise in SQL and modern data modeling techniques - Strong proficiency in Python (or similar) for data transformation, statistical analysis, and ML integration - Experience working with modern data stack tools (e.g., Airflow, dbt, Snowflake, BigQuery, Redshift, Spark) - Proven experience building scalable data warehouse environments Requirements - Strong understanding of statistics, hypothesis testing, and causal inference - Experience designing and operating A/B testing frameworks - Ability to define metrics, KPIs, and experimentation standards across teams - Experience supporting production ML workflows or predictive modeling initiatives - Strong systems thinking with the ability to connect technical decisions to business outcomes Preferred Experience - Experience in restaurant technology, POS systems, or digital ordering platforms - Exposure to customer lifecycle analytics, marketplace analytics, or growth experimentation - Familiarity with real-time data pipelines (e.g., Kafka or similar) - Experience mentoring engineers or leading cross-functional initiatives Why This Role is Unique - You’ll shape the analytical backbone of a fast-scaling product ecosystem - Your work will directly influence revenue, product strategy, and customer experience - You’ll operate at the intersection of engineering rigor and business decision-making - You’ll help build experimentation and ML systems that power millions of restaurant transactions daily

• Design and implement scalable, secure data pipelines across multi-cloud ecosystems (Azure, AWS, GCP). • Develop and maintain ETL/ELT workflows using modern orchestration tools like Apache Airflow, Spark, and cloud-native services. • Build and optimize data processing frameworks with technologies such as Apache Beam, DLT, DBT, and Snowflake. • Collaborate with data scientists and AI engineers to operationalize machine learning models and integrate AI services (Azure AI, AWS SageMaker, GCP Vertex AI, Databricks MLflow). • Implement data quality, governance, and observability practices to ensure accuracy, consistency, and compliance. • Design and maintain data models and analytical layers that power self-service analytics and BI solutions. • Continuously evaluate emerging tools and frameworks to deliver best-in-class performance and cost efficiency.

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.