Data Engineer

• Design and implement end-to-end data pipelines using PySpark, Snowflake, and AWS cloud services
• Architect scalable ELT/ETL workflows and data warehouse models supporting insurance analytics use cases
• Drive data migration and modernization efforts from legacy environments to cloud-native platforms
• Develop and review complex SQL transformations, stored procedures, and data quality validation frameworks
• Establish and enforce data engineering standards, coding best practices, and pipeline documentation
• Provide hands-on troubleshooting and performance optimization across the data stack
• Coordinate day-to-day activities across onshore and offshore data engineering teams to ensure timely delivery
• Serve as a technical point of contact for business stakeholders, translating requirements into engineering deliverables
• Facilitate requirement-gathering sessions, sprint planning, and status updates with project teams
• Communicate project progress, risks, and dependencies to project managers and client stakeholders
• Mentor junior engineers and conduct code reviews to uphold quality standards
• Collaborate with data architects, analysts, and QA teams throughout the project lifecycle

Lead Data Engineer

• Perform data engineering work involving Azure data and analytics services
• Develop and process large-scale data using Python and PySpark
• Build and optimize ETL/ELT pipelines, data models, and data warehouses
• Use SQL for data extraction, transformation, and optimization
• Apply Infrastructure as Code and CI/CD practices

Senior Data Engineer

• Develop rigorous analyses and reviews of consultant design and engineering.
• Manage mechanical and electrical engineers and construction managers deliverables.
• Review and comment on design drawings.
• Coordinate and manage on site engineering issues.
• Assist in developing and managing project strategic plans and program initiatives to meet owner goals and needs.
• Act as Owner’s Advisor with primary responsibility for all project phases.
• Provide MEP expertise to the project management team.
• Manage overall planning, design, and construction processes and orchestrate related activities.
• Guide clients through design and construction complexities, including ownership priorities, construction logistics and sequencing, and design management of architecture and engineering teams.
• Coordinate with architects, contractors, and project team members to meet objectives and resolve issues.
• Lead contract negotiation, drafting, and execution.
• Manage project budget and schedule and report to the project team and client.
• Lead proposal review, leveling, and analysis and present findings to the client.
• Lead consultant and vendor selection, including RFQ/RFP preparation, firm research and comparison, business-term negotiation, and team selection.

Senior MEP Engineer, Data Center

• Design and implement end-to-end data pipelines using PySpark, Snowflake, and AWS cloud services
• Architect scalable ELT/ETL workflows and data warehouse models supporting insurance analytics use cases
• Drive data migration and modernization efforts from legacy environments to cloud-native platforms
• Develop and review complex SQL transformations, stored procedures, and data quality validation frameworks
• Establish and enforce data engineering standards, coding best practices, and pipeline documentation
• Provide hands-on troubleshooting and performance optimization across the data stack

• Shape how discovery data is structured, connected, and made AI-ready across Biologics Discovery
• Design and deliver AI-ready discovery data products supporting ML, AI, and insight generation
• Define scalable integration requirements, transformation patterns, and data schemas for discovery data acquisition, harmonization, and downstream analytics
• Translate scientific and analytical requirements into data product specifications, data contracts, acceptance criteria, and delivery requirements
• Define access and data consumption patterns for analytics, modeling, and agentic AI workflows
• Catalog discovery instruments, data types, and data sources to inform prioritization and integration approaches
• Establish and drive adoption of standards and best practices for data quality, provenance, lineage, reproducibility, metadata, and documentation
• Partner with ontology, data architecture, platform, and AI teams to make discovery data products connected, discoverable, and suitable for advanced analytics and AI applications
• Apply FAIR data principles to make data products reusable, scalable, and interoperable
• Serve as a thought leader in scientific data architecture, harmonization, and AI-ready data practices
• Collaborate with scientists, AI/ML teams, enterprise Data Strategy & Products, Technology teams, and other stakeholders

Principal Data Engineer, Biologics Discovery

• Design and deliver AI-ready discovery data products for ML, AI, and insight generation across Biologics Discovery
• Define and lead scalable integration requirements, transformation patterns, and data schemas
• Enable discovery data acquisition, harmonization, and downstream analytics across systems
• Translate scientific and analytical requirements into data product specifications, data contracts, acceptance criteria, and delivery requirements
• Define access and data consumption patterns for analytics, modeling, and agentic AI workflows
• Catalog discovery instruments, data types, and data sources to inform prioritization and integration approaches
• Establish and drive adoption of standards and best practices for data quality, provenance, lineage, reproducibility, metadata, and documentation
• Partner with ontology, data architecture, platform, and AI teams to connect and improve discovery data products
• Apply FAIR data principles to make data products reusable, scalable, and interoperable
• Serve as a thought leader in scientific data architecture, harmonization, and AI-ready data practices

• Design and implement scalable, secure, and high-performing data architectures on Microsoft Azure
• Architect end-to-end data platforms leveraging Azure services, Snowflake, and dbt
• Define enterprise data models, integration patterns, architecture standards, and reusable frameworks
• Lead data migration and modernization initiatives from legacy platforms to Azure and Snowflake
• Design and optimize ETL/ELT pipelines and orchestration frameworks using Azure Data Factory and dbt
• Establish best practices for data governance, security, metadata management, lineage, and data quality
• Collaborate with business stakeholders, solution architects, and engineering teams to translate requirements into scalable technical solutions
• Drive architecture reviews, performance optimization, technology roadmap planning, and design governance
• Provide technical leadership, guidance, and mentoring to data engineering teams

Data Architect

• Build and maintain large-scale data pipelines and platforms
• Provide support in building robust data pipelines
• Perform data processing and storage using AWS services
• Optimize data pipeline and platform performance

• Lead the design of scalable, secure, and maintainable data and analytics solutions across multiple programs and business domains
• Partner with Engineering, Product Management, Data Governance, Security, platform teams, and business stakeholders to translate strategy into architecture, delivery roadmaps, and reusable implementation patterns
• Define architecture direction, standards, reference patterns, and reusable components for data ingestion, storage, transformation, analytics, and consumption
• Own architecture diagrams, technical designs, integration blueprints, data models, standards, and architecture decision records for the Data and Analytics vertical
• Develop proofs of concept, reference implementations, and reusable patterns to accelerate delivery and reduce technical debt
• Evaluate emerging technologies, platforms, frameworks, and advanced analytics capabilities and recommend fit-for-purpose adoption
• Conduct architecture, design, and code reviews
• Embed data privacy, access control, governance, and compliance requirements into solution designs
• Resolve complex performance, quality, scalability, integration, reliability, and operational challenges
• Mentor engineers and architects through solution design, implementation planning, technical evaluations, and delivery reviews
• Translate strategic requirements into technical designs, delivery roadmaps, and phased implementation plans
• Apply enterprise data architecture practices across data warehousing, Lakehouse, real-time streaming, data engineering, integration, analytics, and operational support
• Analyze cross-functional technical challenges and explain architecture decisions and trade-offs to technical and executive audiences
• Influence technical decisions through expertise, collaboration, and hands-on leadership

Principal Architect, Data Product Delivery

• Lead moderately complex initiatives and deliverables within technical domain environments
• Contribute to large-scale strategy planning
• Design, code, test, debug, and document technology projects and programs, including upgrades and deployments
• Review moderately complex technical challenges and evaluate technologies and procedures
• Resolve technical issues while leveraging knowledge of functions, policies, procedures, and compliance requirements
• Lead a team to meet existing or potential client needs
• Collaborate with peers, colleagues, business stakeholders, and mid-level managers
• Lead projects and act as an escalation point
• Provide guidance and direction to less experienced staff
• Develop and support technology solutions for identifying, measuring, aggregating, and reporting counterparty credit risk