Data Engineer

• Own and evolve the enterprise customer domain model across conceptual, logical, canonical, and physical layers
• Define canonical customer entities, identifiers, hierarchies, and relationships across CRM, billing, CIS, and downstream systems
• Govern model evolution to support new business capabilities while preventing redundancy and fragmentation
• Resolve semantic conflicts and enforce consistent customer definitions enterprise‑wide
• Own customer‑centric data products including curated datasets, views, and domain data assets
• Define and maintain data product contracts (schemas, SLAs, quality rules, access patterns, and usage expectations)
• Partner with engineering and analytics teams to ensure data products are production‑ready, discoverable, and reusable
• Design customer data products to support analytics, reporting, and AI/ML use cases
• Ensure alignment with enterprise data platform standards (lakehouse, catalogs, governance frameworks)
• Guide ingestion, transformation, and persistence patterns to preserve model integrity and performance
• Collaborate with application, integration, and analytics architects to embed consistent customer data across solutions
• Define and enforce data quality standards, validation rules, and stewardship processes
• Partner with data governance teams on metadata, lineage, classification, and access controls
• Enable downstream teams through clear documentation, diagrams, and reference models
• Act as a subject‑matter expert for customer data during design reviews and delivery engagements

Senior Data Architect – Customer Domain

• Serves as a lead in the analysis of business requirements associated with the design and implementation of highly complex, highly scalable enterprise data processes
• Designs and develops robust and efficient processes for extracting, loading and transforming data to meet the needs of enterprise scale projects
• Provides guidance and participates in activities involved in the analysis, design, development, troubleshooting, debugging and maintenance of an enterprise data management and data ecosystem
• Identifies, investigates, tracks, and resolves the most complex technical errors ensuring the solution is timely and of acceptable quality
• Establishes and enforces database standards, guidelines, procedures and quality assurance for database deliverables such as conceptual design, logical data model capacity planning, external data interface specs, data loading, data maintenance plan and security
• Reviews and analyzes data objects and models to improve data management and data access
• Designs, constructs, tests and implements extraction and transformation programs, processes and cycles for historical and current source data
• Creates technical specification for supporting enterprise data transformation processes and supplementing system documentation for ETL processes
• Performs data profiling, source-target mappings, data transformation development, optimization, testing and implementation
• This position participates and serves as a lead in maintaining the ETL environment for the company’s system-wide enterprise data warehouse.

Supply Chain Data Engineer

• Design and implement data models, schemas, and ontologies for chemical, biological, and automation-generated data that serve discovery workflows across the portfolio.
• Define and maintain controlled vocabularies, metadata standards, and FAIR-compliant data frameworks in partnership with Preparedness4Insight.
• Implement semantic data standards (RDF, OWL, SPARQL) and ontology engineering practices to create interoperable, machine-readable scientific data.
• Design and implement data lakehouse architecture using modern platforms (Databricks, Snowflake, or equivalent).
• Build and optimize ETL/ELT pipelines using Spark, dbt, or similar tools.
• Implement real-time and streaming data integration (Kafka, Kinesis) connecting LIMS, instruments, and lab automation systems to the data infrastructure.
• Design and implement knowledge graphs (Neo4j, Amazon Neptune, TigerGraph) that capture molecular, target, pathway, and experimental relationships across the discovery landscape.
• Architect specialized data solutions: array databases (TileDB) for genomics/imaging, document stores (MongoDB) for experimental records, and vector databases for embedding-based retrieval supporting ML and RAG workflows.

Advisor – Data Architect, Data Foundry

• Design and develop data serving and semantic layers to support business needs
• Develop data models for use by analysts and applications using modern dimensional modeling practices
• Build and maintain robust, scalable data pipelines using ETL, ELT processes
• Create efficient and performant data models to support business requirements
• Implement and automate data quality checks and monitoring to ensure data accuracy
• Manage, troubleshoot and optimize existing data infrastructure, including cloud-based solutions
• Leverage cloud data platforms (Fabric, Databricks, Snowflake, etc.) for data storage, processing, and analysis
• Collaborate with data analysts, data scientists, and business stakeholders to understand their data needs

• Define and drive enterprise data architecture strategy
• Establish target-state data platform architectures and standards aligned to business priorities.
• Translate business requirements into scalable data architecture solutions.
• Design and govern enterprise data ecosystems
• Architect data solutions and integration strategies across systems.
• Ensure consistent access, integration, and consumption of data across platforms.
• Lead data architecture governance and quality initiatives
• Define standards for data quality, metadata, master data, and lifecycle management.
• Establish data governance policies, compliance controls, and security practices.
• Drive platform and technology decisions
• Evaluate and recommend data platforms, tools, and emerging technologies.
• Define standards for data integration, pipelines, and data services.
• Collaborate across business, data, and technology teams
• Partner with stakeholders to align data architecture with enterprise objectives.
• Act as a bridge between data strategy and technical execution.
• Provide leadership and oversight
• Lead architecture reviews and ensure adherence to standards
• Support delivery teams through the product lifecycle.

Enterprise Data Architect

• Design, build, and deploy AI-enabled applications that support real operational workflows and decision-making for DoW, federal, and enterprise environments.
• Develop application logic that integrates AI models, APIs, data pipelines, user interfaces, and enterprise systems.
• Implement retrieval-augmented generation, semantic search, embeddings, vector databases, prompt strategies, and agent-based workflows where appropriate.
• Integrate structured and unstructured enterprise data sources into AI-enabled applications.
• Build orchestration workflows that connect foundation models, machine learning services, tools, APIs, and secure enterprise environments.
• Develop evaluation frameworks to measure AI output quality, grounding, accuracy, traceability, and failure modes.
• Implement guardrails, validation checks, fallback logic, and human-in-the-loop review points to support reliable AI behavior.
• Support training, tuning, evaluation, and validation of AI or machine learning models using enterprise data while ensuring outputs remain defensible, explainable, and aligned to approved methodologies.
• Develop secure APIs, backend services, dashboards, visualizations, and integration components for AI-enabled analytic tools and decision-support platforms.
• Implement monitoring, logging, observability, model performance tracking, and cost controls for AI systems in operation.
• Advise on model transparency, explainability, auditability, data governance, information assurance, and access controls required for secure DoW and federal deployment.
• Collaborate with functional SMEs to translate business needs, mission requirements, workflow requirements, review expectations, and operational constraints into technical requirements.
• Contribute reusable AI components, accelerators, reference architectures, and delivery patterns for future enterprise and federal AI implementations.
• Support demonstrations, pilots, proofs of value, testing, deployment readiness, and user acceptance activities with internal and client stakeholders.

AI/Data Engineer

• Serve as a hands-on technical leader and delivery manager designing and implementing modern cloud-native data platforms for federal clients.
• Lead architecture decisions, mentor engineers, and ensure high-quality, secure, and scalable delivery aligned to mission outcomes.
• Architect and lead delivery of AWS-based lakehouse solutions using Databricks.
• Design, build, and optimize ETL/ELT pipelines leveraging Databricks, Apache Spark, Delta Lake, Python, and SQL.
• Define and enforce data architecture standards including ingestion patterns, performance tuning, and cost optimization.
• Implement production-grade engineering practices, including data quality validation, reconciliation, and monitoring.
• Apply data security and governance controls appropriate for federal and defense environments.
• Partner with client stakeholders and product owners to translate mission and business requirements into technical designs.
• Lead and mentor data engineers through code reviews, architecture reviews, and delivery execution.
• Establish and mature CI/CD pipelines for Databricks workloads and supporting infrastructure.

• Work within an multidisciplinary team consisting of both developers and researchers
• Assembles large, complex sets of data to support AI/ML algorithm implementation
• Builds required infrastructure for optimal extraction, transformation and loading of data from various data sources
• Curate and maintain data that is stored in support of metrics and evaluation
• Implement Artificial Intelligence/Machine Learning algorithms
• Identifies, designs, and implements internal process improvements including re-designing infrastructure for greater scalability, optimizing data delivery, and automating manual processes
• Using Agile methodologies to develop software

Senior Data Engineer

• Manage a team by hiring and onboarding talent, setting clear expectations, conducting formal performance reviews, and coaching accountability.
• Lead the team to deliver on the enterprise data platform roadmap by translating architecture direction into sequenced execution plans, team backlogs, and measurable outcomes.
• Create a strong engineering culture across the team by setting standards for maintainability, organization, and repeatability; growing engineers via regular 1:1s, timely feedback, development plans, and formal review cycles; and being responsible for overall team performance and cohesion.
• Own day-to-day execution for the Data Engineering team by breaking work into tasks, driving sprint-level planning, delegating effectively to grow ownership, unblocking delivery, and ensuring high-quality outcomes through engineering reviews and feedback.
• Drive implementation of the Medallion architecture (Bronze → Silver → Gold) with strong enforcement of layer responsibilities, ensuring repeatable and auditable pipeline behavior leveraging Boomi, Dbt, Azure Data Bricks, Boomi/Rivery, Delta tables, and Sql Server.
• Partner closely with the Data Architect to bring the architecture to life through concrete implementation patterns, reference pipelines, and guardrails—ensuring the team can consistently execute the intended approach.
• Reduce friction by driving crisp decisions and forward motion—using lightweight decision records, seeking the right input, and then executing accountability (while avoiding overly heavy process).
• Establish DataOps practices aligned with modern software development lifecycle (SDLC): CI/CD for data assets, consistent branching/release patterns, code review standards, and runbooks/operational readiness for pipelines.
• Improve pipeline observability and operational reliability by implementing monitoring for freshness/staleness, failure modes, and quality signals, and ensuring the team responds predictably to incidents and support requests.
• Drive stakeholder partnership and intake stream: collaborate with business partners to clarify requirements, reduce ad-hoc thrash, and shape requests into deliverable, well-scoped work that fits the platform strategy.
• Develop future leaders and succession plans for key roles through assigning regularly occurring responsibilities, mentoring, and strategic delegation.
• Support and promote the company values through positive interactions with both internal and external stakeholders on a regular basis.
• Other strategic business initiatives or special cross-functional project involvement as required.

Manager, Data Engineering

• Mentor, train, and coach Interns and Consultants
• Facilitate completion of project work plans
• Identify areas of risk and opportunities to increase efficiency
• Strengthen client relationships and deepen communication skills
• Accept responsibility for decision-making regarding project scope and work plans
• Develop modern data engineering solutions using Microsoft technologies

Senior Data Engineer, Analytics

• Design and deliver scalable data pipelines in AWS and Databricks that drive reliable, production-ready data solutions
• Proactively monitor, troubleshoot, and resolve data pipeline issues to ensure consistent system performance and data integrity
• Triage and resolve incidents based on business impact, ensuring timely and effective issue resolution
• Partner with business stakeholders to investigate and explain data discrepancies, improving trust in data assets
• Develop ad hoc analyses and reports using SQL and Python to support critical business decisions
• Automate manual processes to improve efficiency, reduce errors, and accelerate delivery timelines
• Collaborate across engineering, platform, and product teams to manage dependencies and resolve service issues
• Deliver solutions within an Agile (Kanban/DevOps) environment, contributing to continuous improvement and faster delivery cycles

Software Engineering Advisor – Data Engineer

• Lead and mentor an interdisciplinary team consisting of both developers and researchers
• Assemble large, complex sets of data to support AI/ML algorithm implementation
• Build required infrastructure for optimal extraction, transformation and loading of data from various data sources
• Curate and maintain data used for metrics and evaluation
• Implement Artificial Intelligence/Machine Learning algorithms
• Identify, design, and implement internal process improvements including re-designing infrastructure for greater scalability
• Optimize data delivery and automate manual processes
• Use Agile methodologies to develop software

Staff Data Engineer

• Work within an multidisciplinary team consisting of both developers and researchers
• Assemble large, complex sets of data to support AI/ML algorithm implementation
• Build required infrastructure for optimal extraction, transformation and loading of data from various sources
• Curate and maintain data that is stored in support of metrics and evaluation
• Implement Artificial Intelligence/Machine Learning algorithms
• Identify, design, and implement internal process improvements including re-designing infrastructure for greater scalability, optimizing data delivery, and automating manual processes
• Use Agile methodologies to develop software

• Build and maintain high-performance pipelines using Airflow, dbt, and Python to organize raw data for analysis, modeling, and product use cases
• Partner with Product, Data Science, and ML Engineering to build the infrastructure supporting machine learning workloads, including training datasets, feature pipelines, and model output tables
• Write production-grade SQL (Amazon Redshift) and manage query optimization to support rapidly growing business needs
• Build instrumentation, monitoring, and alerting to guarantee data freshness, quality, and availability in production environments
• Identify and implement improvements by automating manual tasks and reducing pipeline runtimes
• Utilize AI-assisted developer tools (like Claude Code) daily to accelerate development and participate in design reviews and on-call rotations

• Design, develop, and maintain scalable ETL/ELT pipelines for high-volume batch and real-time data
• Build and optimize analytics infrastructure that supports developer productivity and engineering metrics
• Partner with cross-functional teams to deliver data products and self-service insights
• Develop and maintain data models, schemas, and architectures for modern data platforms
• Ensure reliability, performance, and data quality across pipelines and platforms
• Contribute to DevOps and CI/CD practices for data and backend services
• Document designs, workflows, and best practices to support team knowledge sharing

• Deploy new data pipelines
• Design & build data observability platforms and metrics
• Build metadata driven pipeline solutions
• Monitor and optimize data transformations
• Build third party data integrations

• Implement and maintain data contract specifications that codify table ownership, schema expectations, and quality thresholds.
• Enforce standards through automated validation in the deployment pipeline.
• Collaborate with data owners to improve metadata coverage across the team's data estate.
• Contribute to an automated scoring pipeline that evaluates data assets on their readiness for AI and analytical use.
• Build and maintain the jobs that collect quality signals and surface scores to data teams.
• Support the discovery and classification of sensitive data across the data estate.
• Maintain tagging and lineage automation frameworks.
• Monitor AI usage across the company, defining new avenues for AI assistance.
• Build systems that make people actually trust AI outputs regarding data, including query accuracy checks and clear attribution.
• Build and maintain the semantic framework that makes data assets more legible to both people and AI systems.
• Proactively leverage AI tools to accelerate development, maintain code quality, and explore new approaches to data engineering problems.
• Partner with adjacent teams to onboard data assets into the AI-ready platform.

• Supports the development, optimization, and maintenance of Cushman & Wakefield’s commercial real estate (CRE) forecasting infrastructure across the Americas.
• Engineering robust data pipelines, automating model workflows, and ensuring the integrity and scalability of forecasting systems.
• Operate as a self-sufficient data practitioner, capable of independently delivering data solutions or working side-by-side with technology teams to ensure alignment and production readiness of QIG capabilities.
• Prototype, build and maintain automated data pipelines for ingesting, transforming, and storing CRE and macroeconomic datasets used in forecasting models.
• Ensure data integrity and consistency across all QIG’s inputs and outputs through rigorous validation and quality control procedures.
• Create and maintain documentation of any synthetic data model architecture, data flows, and diagnostic procedures.
• Develop internal documentation and process automation, and serve as expert on the integration, application and processing of internal data, 3rd party vendor data and other public data.

• Design, build, and maintain scalable Snowflake-based data warehouse architectures, ensuring performance, reliability, and extensibility for future growth.
• Own data architecture and technical design decisions, including data models, integration patterns, and optimization strategies.
• Transform raw source data from multiple internal and external systems into analytics-ready data models using industry best practices.
• Lead and contribute to warehouse development using dimensional modeling techniques (e.g., star schemas, slowly changing dimensions).
• Develop and manage data pipelines using Snowflake features such as Snowpipe, tasks, stored procedures, and performance optimization techniques.
• Manage and monitor data integrations with internal systems via APIs, application calls, or custom code to ensure data accuracy and reliability.
• Apply auditing, change tracking, error handling, and monitoring across large datasets to maintain high data quality standards.
• Identify and resolve data quality issues and implement controls to distinguish valid vs. invalid data used in decision-making.
• Leverage Python and application knowledge to interact with and extract data from other systems.
• Provide technical leadership through coaching and mentoring junior and mid-level data engineers, including code reviews, design feedback, and best-practice guidance.
• Contribute to and help define data engineering standards, best practices, and documentation across the team.
• Collaborate across teams to support day-to-day operational data needs and continuously improve data pipelines and processes.

• Own End-to-End Delivery of Core Data Platform Components
• Design and ship the data normalization, schema mapping, validation, enrichment, and distribution pipeline for a net-new intelligent data warehouse
• Write production code as a hands-on individual contributor. This is not a role that delegates implementation to others
• Take technical ownership from architecture through deployment, with accountability for reliability, performance, and correctness
• Drive Technical Architecture
• Partner with a small seed team of senior engineers to define the end-to-end architecture for an AI-native data warehouse serving institutional financial clients
• Bring opinionated decisions on schema design, normalization strategies, API exposure patterns, and data distribution approaches
• Evaluate and select technologies with a bias toward what ships well and scales sustainably
• Build AI Evaluation Infrastructure
• Design and implement the evaluation framework that makes AI-generated outputs trustworthy in high-stakes financial data contexts
• Build cross-model comparison tooling, deterministic validation checks, and human-in-the-loop review workflows
• Contribute to shared AI infrastructure that will serve as a foundation for multiple products across the company
• Ship with AI-Native Development Practices
• Use agentic coding tools and LLM-assisted development as your primary workflow; this is how the entire team operates
• Bring strong opinions about how to get the most out of AI-assisted development while maintaining quality and reliability
• Contribute to the team’s evolving practices around AI-accelerated SDLC
• Establish Technical Standards
• As an early senior voice on a new team, set coding standards, review practices, and architectural documentation that will scale
• Help define what “good” looks like for a team building at speed without sacrificing quality
• Mentor and provide technical guidance as the team grows from seed squad to full delivery organization
• Collaborate Across Engineering and Product
• Work closely with your engineering manager, peer engineers, and product management to translate requirements into executable technical plans
• Participate actively in design reviews and roadmap discussions with grounded, implementation-level perspective
• Engage directly with the nuances of private markets data – fund administration, investment data schemas, institutional reporting workflows – to build domain intuition that improves your technical decisions

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Senior Data Architect – Customer Domain

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Senior Data Engineer

Manager, Data Engineering

Senior Data Engineer, Analytics

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Staff Data Engineer

Senior Data Engineer

Data Engineer

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Data Engineer

Data Engineer

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