Data Product Engineer, Global Data and AI Enablement

PharmaEssentia U.S.A.•Burlington, MA

1d•$116,000 - $150,000•Hybrid

About The Position

PharmaEssentia’s Global Data & AI Enablement team is seeking a hands-on Data Product Engineer to build and manage integrated data products for internal customers. Reporting to the Manager, Data Product Engineering, this role transforms vendor-delivered raw data in the data warehouse into curated, reusable, documented data tables that support commercial analytics, business operations, and AI applications. The engineer will integrate pharmaceutical commercial data sources, codify business rules, and develop reliable data products and rules-based alerts that provide timely, actionable information to field and business teams. This builder role is suited to someone who translates business needs into durable technical solutions and owns data products throughout their lifecycle. The successful candidate will partner with cross-functional teams to design, deploy, monitor, and enhance production data products. The work aims to reduce repeated manual data preparation, improve the consistency and speed of internal decision-making, and create trusted data assets that can be reused across analytics and AI use cases. The position is based in Burlington, Massachusetts, with a hybrid work model requiring on-site presence three days per week. The expected base salary range is $116,000 to $150,000. Eligible candidates may also receive short-term and long-term incentives, in addition to a competitive benefits package.

Requirements

Bachelor’s or Master’s degree in computer science, data engineering, data science, information systems, engineering, or a related field, or equivalent practical experience.
At least 2 years of hands-on experience in data engineering, analytics engineering, data product engineering, or a closely related role, with demonstrated ownership of production data products.
Strong SQL and hands-on Python skills for data transformation, business-rule implementation, automation, testing, troubleshooting, and performance optimization.
Hands-on experience with Snowflake or a comparable cloud data warehouse and modern ELT patterns.
Excellent communication and collaboration skills, including the ability to work effectively with technical teams, business stakeholders, and external data vendors.
Strong ownership mindset, attention to detail, and commitment to building reliable, documented, and maintainable data products.

Nice To Haves

Experience with dbt or a comparable transformation framework.
Pharmaceutical, biotechnology, or healthcare commercial-data experience, including claims, CRM, HCP/HCO, sales and distribution, Specialty Pharmacy/HUB, payer/access, or marketing data.
Experience delivering data products to downstream BI, CRM, field-facing, workflow, or application environments.
Experience designing dimensional, relational, or analytics-oriented data models and producing reusable, curated datasets from complex multi-source data.
Experience translating business requirements into production data products, including rules-based workflows, operational datasets, alerts, or decision-support outputs.

Responsibilities

Design, build, test, deploy, and maintain data transformations, curated data tables, reusable models, semantic-layer components, and production-ready datasets in Snowflake.
Build integrated data products that combine claims, Veeva CRM, HCP/HCO hierarchies, Specialty Pharmacy/HUB, specialty distributor/3PL, payer/access, sales, and marketing-engagement data.
Translate internal customer requirements and product specifications into scalable technical designs under the direction of the Manager, Data Product Engineering.
Codify approved business rules and develop supporting datasets, workflows, and rules-based alerts that surface relevant opportunities, exceptions, or actions to field and business teams through approved downstream channels.
Develop reusable data models with consistent definitions to support multiple analytics, operational, and AI use cases.
Implement automated testing, reconciliation, data-quality controls, monitoring, and alerting; investigate root causes and coordinate issue resolution with Data Foundations & Integration and external vendors.
Use SQL and/or Python to transform complex datasets, automate recurring processes, implement business logic, optimize queries, and reduce manual data preparation.
Apply version control, peer review, deployment controls, and automated delivery practices to improve the reliability and maintainability of production data products.
Optimize data models and transformation workflows for performance, scalability, reliability, maintainability, and Snowflake cost efficiency.
Support production monitoring, incident resolution, and ongoing enhancements to existing data products based on user feedback, changing business needs, and source-data changes.
Maintain clear documentation for data sources, lineage, transformations, business rules, data models, quality controls, dependencies, and intended product use.
Collaborate cross-functionally to provide trusted datasets and interfaces for predictive analytics, AI applications, and agent-based solutions.