Top Consulting Data Engineering Jobs in Boston, MA

Leads applied AI and health data architecture initiatives for payer clients. Designs cloud-native platforms, pipelines, data models, integrations, and modernization roadmaps across AWS, Azure, Databricks, and Snowflake. Oversees data quality, governance, anonymization, performance optimization, and legacy migrations while advising stakeholders on AI-ready architectures. Manages multi-team delivery, resolves technical issues, presents solution tradeoffs, and coaches practitioners.

Lead the design and implementation of enterprise-scale Knowledge Graphs and Semantic AI solutions, integrating graph intelligence and Generative AI. Manage cross-functional teams in data architecture and governance.

Design and deliver scalable data and analytics solutions supporting the end-to-end sales lifecycle. Integrate SAP, Salesforce, and other enterprise sources into cloud data lakes, modernize BI platforms, ensure data quality and governance, drive automation and process improvements, and provide technical leadership and mentorship to teams while partnering with business and IT stakeholders.

Lead the redesign and modernization of a legacy database system for a financial services client. Analyze existing architecture, design scalable data models and schemas, optimize tables and stored procedures, implement governance and security controls, and ensure performance, auditability, and regulatory compliance. Partner with stakeholders in an Agile environment, support iterative development, and mentor team members on database modeling best practices.

Create, enhance, and coordinate database schema migrations; conduct code reviews and performance monitoring; and collaborate with junior database administrators on database architectures, coding standards, and quality assurance policies. The role requires travel or relocation to various U.S. client sites for short- and long-term assignments.

Leads enterprise information architecture, business intelligence, and data warehouse strategy and delivery. Oversees BI and data warehouse teams, establishes roadmaps and governance, mentors technical staff, manages project priorities, and ensures high-quality implementation. Designs enterprise data models, datamarts, metadata, ETL processes, reporting strategies, and integration artifacts. Partners with business and technology stakeholders to align data solutions with strategic goals, improve adoption, track value, and drive continuous improvement.

Own and evolve the enterprise data platform by architecting scalable data models, integrations, warehouses, and pipelines. Build and maintain solutions using Snowflake, dbt, Fivetran, Airflow, AWS, and Power BI. Integrate diverse data sources, implement monitoring and data quality frameworks, troubleshoot production issues, and deliver curated datasets for analytics and AI use cases. Provide technical leadership, establish engineering standards, and collaborate with business, governance, QA, and analytics teams.

Leads legacy data migration into SaaS platforms by assessing sources, designing conversion strategies, executing ETL workflows, mapping and transforming data, validating integrity, and managing PII. Engages clients to gather requirements, document business processes, support governance and data quality, coordinate implementation teams, and develop reusable methodologies and training materials.

Leads data engineering for Payment Operations monitoring transformation. Designs and maintains Snowflake pipelines, tables, stored procedures, monitoring scenarios, KPI infrastructure, and data quality controls. Serves as the Snowflake subject matter expert, documents architecture and operational processes, guides technical partners, and translates business needs into scalable data solutions. The role requires production data engineering experience, advanced SQL, Snowflake expertise, job scheduling, pipeline operations, and stakeholder communication.

Lead scientific data architecture for biopharmaceutical client platforms. Develop information models, scalable relational and NoSQL integration strategies, analytics environments, ontologies, and taxonomies. Translate architectures for technical and business audiences, abstract common solutions across client environments, and serve as a subject matter expert and thought leader for scientific data modeling methodologies.