Data Engineer

• Design, build, and maintain scalable data ingestion pipelines supporting a wide range of source systems (APIs, databases, streaming platforms, third-party data providers)
• Develop batch and real-time ingestion frameworks capable of handling high data volumes and low-latency requirements
• Build fault-tolerant and resilient pipelines with strong guarantees around data integrity, idempotency, and recovery
• Establish and enhance observability, monitoring, and alerting for ingestion pipelines (latency, throughput, failures, data freshness)
• Implement and enforce data quality checks and validation frameworks at ingestion points
• Contribute to the development of Data Ingestion as a platform product, including reusable frameworks, standards, and best practices
• Leverage AI-assisted development tools and automation to accelerate pipeline development, improve code quality, and enhance operational efficiency (e.g., code generation, testing, anomaly detection, workflow automation)
• Partner with upstream system owners and downstream consumers to define data contracts, SLAs, and schema evolution strategies
• Optimize pipelines for performance, cost, and efficiency across compute and storage layers
• Use Infrastructure as Code (IaC) to provision and manage ingestion infrastructure in a consistent and scalable manner
• Collaborate with platform, analytics, and data science teams to ensure seamless data availability and usability.

• Work closely with experienced engineers and analysts to build reliable data pipelines
• Shape how data flows across the organization
• Write code daily, solving real data challenges
• Contribute to scalable data solutions—not just maintaining systems
• Collaborate with a team where learning and mentoring are part of the culture
• Gain hands-on experience with modern data tools and technologies
• Build a solid foundation for a long-term career in data engineering
• Contribute to meaningful projects that support real business decisions

• Design and deliver scalable data solutions that power decision-making across the organization.
• Take ownership of key components of the data platform, working hands-on with modern tools and technologies to build reliable, high-quality data pipelines.
• Collaborate with teams across the business, contribute to architectural decisions, and support the growth of other engineers through mentorship and knowledge sharing.
• Work on meaningful data challenges that have real business impact.
• Grow your leadership skills through mentoring and project ownership.

Senior Data Engineer

• Own the strategy, design, and operational rollout of a Customer Data Platform (CDP) capability
• Drive capture, unification, activation, and governance of customer and digital touchpoint data
• Architect identity resolution and profile management approaches
• Lead integrations with SFDC, SFMC, 3rd Party SaaS, and SAP
• Partner with analytics, privacy, and commercial stakeholders to enable real-time personalization
• Build audience definitions and activation paths for on-site personalization, SFMC campaigns, CRM actions, and downstream systems
• Produce runbooks, onboarding guides, and self-service tooling for marketers, CRM owners, and platform teams.
• Lead cross-functional teams to deliver CDP capabilities.

Customer Data Platform Lead

• Own the strategy, design, and operational rollout of a Customer Data Platform (CDP) capability
• Drive capture, unification, activation, and governance of customer and digital touchpoint data
• Architect identity resolution and profile management approaches
• Lead integrations with SFDC (CRM), SFMC (Marketing Automation), 3rd Party SaaS, and SAP (ERP)
• Partner with analytics, privacy, and commercial stakeholders to enable real-time personalization
• Define the CDP vision, roadmap, and prioritized use cases aligned to commercial and digital objectives
• Establish canonical data models, event taxonomy, identity graphs, and profile schemas required for personalization and measurement
• Design robust data capture from Sitecore AI DXP, web/mobile events, product catalogs, clinical interactions, and other touchpoints
• Manage Sitecore CDP relationships and third-party integration partners

• 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

• Enable data-driven decision making by collecting, transforming and visualizing the data
• Responsible for protecting, securing, and proper handling of all confidential data
• Design, build and maintain data structures and data processing systems
• Implement and troubleshoot deployments
• Create conceptual, logical and physical data models
• Design and build data ingestion tools, metrics, alerts and notifications

• Architect and develop robust, scalable ETL/ELT pipelines using Cloud Dataflow, Cloud composer (Airflow), and Pub/Sub for both batch and streaming use cases
• Leverage BigQuery as the central data warehouse and design integrations with other GCP services (e.g., Cloud storage, Cloud functions)
• Build and optimize analytical data models in BigQuery
• Implement partitioning, clustering, and materialized views for performance and cost efficiency
• Ensure compliance with data governance, access controls, and IAM best practices
• Develop integrations with external systems (APIs, flat files etc.) using GCP-native or hybrid approaches
• Utilize tools like Dataflow or custom Python/Java services on Cloud Functions or Cloud Run to handle transformations and ingestion logic
• Build automated CI/CD pipeline using Cloud Build, GitHub Actions, or Jenkins for deploying data pipeline code and workflows
• Set up observability using Cloud Monitoring, Cloud Logging, and Error Reporting to ensure pipeline reliability
• Lead architectural decisions for data platforms and mentor junior engineers on cloud-native data engineering patterns
• Promote best practices for code quality, version control, cost optimization, and data security in a GCP environment
• Drive initiatives around data democratization, including building reusable datasets and data catalogs via Datapelx or Data Catalog

• Design and Develop Data Solutions
• Build, maintain, and optimize data pipelines and workflows that support reporting, analytics, and business applications using SQL, Informatica, IICS, and Snowflake.
• Data Processing and Preparation
• Develop and manage data preparation routines for extracts, transformations, and reporting datasets, ensuring reliability and performance.
• Data Modeling and Optimization
• Create and maintain logical and physical data models following normalization principles and best practices.
• Tune queries for optimal performance across Snowflake and Oracle environments.
• Integration and Automation
• Work with various structured and semi-structured data formats (XML, JSON) and leverage AWS cloud frameworks for scalable data integration and processing.
• Data Security and Quality
• Ensure compliance with data security policies and implement best practices for data protection, integrity, and confidentiality.
• Collaboration and Leadership
• Partner with data analysts, developers, and business stakeholders to define data requirements, streamline processes, and improve data accessibility for reporting tools such as Power BI.
• Continuous Improvement
• Recommend and implement improvements to data architecture, workflows, and governance processes.
• Serve as a mentor to junior team members and promote a culture of curiosity and continuous learning.

• Collaborate with and across Agile teams to design, develop, test, implement, and support technical solutions in full-stack development tools and technologies
• Work with a team of developers with deep experience in machine learning, distributed microservices, and full stack systems
• Utilize programming languages like Java, Scala, Python and Open Source RDBMS and NoSQL databases and Cloud based data warehousing services such as Redshift and Snowflake
• Share your passion for staying on top of tech trends, experimenting with and learning new technologies, participating in internal & external technology communities, and mentoring other members of the engineering community
• Collaborate with digital product managers, and deliver robust cloud-based solutions that drive powerful experiences to help millions of Americans achieve financial empowerment
• Perform unit tests and conduct reviews with other team members to make sure your code is rigorously designed, elegantly coded, and effectively tuned for performance

Lead Data Engineer

• Participating in developing and implementing an overall organizational data strategy. The strategy includes data architecture, data management standards, implementation and management of data warehouses, and data analytics systems.
• Creating data architecture within the organization, applying architectural principles to ensure the IT solutions are secure, reliable, scalable, and sustainable, and developing a cohesive technical vision with IT standards.
• Collaborating closely with IT Infrastructure, Bioinformatics, and BlueRock functional groups to ensure business needs are actively being aligned with our solution, and both business and technical requirements are understood and documented by all stakeholders.
• Architecting, creating, managing, and optimizing data pipelines, designing compute workflows and storage solutions for a variety of data assets, and developing integrated data solutions to drive success in analytics workflows.
• Designing and implementing comprehensive solutions for transferring, storing, delivering, and optimizing R&D, TechOps, and MFG data, and associated data specification, documentation, SOP, WIs etc., using public cloud technology (AWS). Design, implement, and maintain data pipelines, ETL workflows, analytical solutions, and respective cloud and computing environments; or implement and configure third-party solutions.
• Working closely with IT teams and vendors to design, implement, and/or improve data integration capabilities that are scalable and maintainable and deliver optimal value based on agreed-upon requirements.
• Implementing and owning data integration across our application stack that keeps data fresh and FAIR.
• Creating reference diagrams, charts, and documentation for the architecture.
• Ensuring quality, availability, and integrity of code, solutions, and respective systems and integrating proactive strategies and best practices regarding data integrity, security, scalability.
• Actively identifying new technologies and practices in the domain through internal and external network. Telecommuting permitted.

Senior Data Architect

• Architect and build the core Growth Analytics data environment with Snowflake as the central platform
• Design, implement, and maintain scalable data pipelines, integrations, and transformation workflows across internal business, product, operational, sales, marketing, finance, and planning systems
• Build trusted, reusable data models and curated datasets to support internal analytics, reporting, forecasting, growth planning, and Strategy & Operations workflows
• Develop and manage BI environments in Power BI and Looker, including semantic models, governed datasets, dashboard foundations, access patterns, and performance standards
• Build AI-ready data layers to support internal intelligence solutions, including structured datasets, metadata, documentation, business definitions, and governed access patterns
• Establish standards for data quality, testing, observability, lineage, documentation, naming conventions, metric definitions, and production reliability
• Partner with internal stakeholders to translate business questions into scalable data products, reporting layers, and analytics-ready infrastructure
• Create and maintain clear documentation for source systems, pipelines, data models, metric definitions, BI assets, and AI-ready datasets
• Lead technical design discussions, make architecture recommendations, and mentor analytics, BI, and data partners on scalable data practices
• Help move AOS from fragmented reporting and manual data processes toward a reliable, governed, and scalable intelligence layer

Principal Data Engineer, Growth Analytics

• Lead the Enterprise Platform data engineering function—defining the roadmap, operating model, and standards that implement JLL’s company data strategy and data quality programs at enterprise scale.
• Align team execution with JLL’s enterprise data strategy while delivering the platform capabilities that all business units depend on.
• Define and drive the Enterprise Platform data strategy—implementing JLL’s company data strategy and consolidating siloed data sources into a unified, governed, and scalable data architecture.
• Provide technical leadership across the Enterprise Platform Data Engineering team and related initiatives, setting architectural standards, design patterns, and best practices.
• Architect sophisticated integration strategies for structured and unstructured data sources.
• Design and implement enterprise-grade data integration frameworks.

Senior Manager, Data Engineering

Lead Data Engineer – Finance Tech

• Own a Data, AI/ML product portfolio spanning Personalization and Marketing
• Define and drive the technical roadmap, balancing innovation, and agile delivery with operational stability and reliability
• Lead the strategic shift from rule-based campaigns to AI and agent-based orchestration
• Oversee full-scale AI/ML & Data pipelines including deep-learning models and optimization capabilities
• Lead architecture reviews, design forums, and technical sessions across Data Science, Engineering, and Technology teams
• Ensure engineering standards are maintained across the portfolio - covering solution design, security, and Data, AI Ops practices
• Drive agile delivery within the Squad model, managing dependencies across a matrixed environment, and removing blockers
• Coach and grow engineering leaders and senior ICs and build a strong internal talent pipeline
• Serve as a trusted partner to Senior stakeholders across Data Science and Business teams
• Translate complex technical concepts into clear, business-relevant narratives for Executive and non-technical audiences

Lead Director – Data Engineering

• Lead data engineering delivery to build reliable, scalable pipeline leveraging Snowflake, Sigma and other applications to support complex data analytics and self-service reporting.
• Drive integration with popular tools and services in the broader data warehousing ecosystem.
• Optimize architectural performance across the Data stack by refining execution parameters and enforcing best practice strategies to maximize resource efficiency and throughput.
• Establish and evolve a data governance operating model across data quality, lineage, metadata management, stewardship, cataloging, and lifecycle management.
• Perform advanced code-level analysis and resource profiling to identify and mitigate systemic root causes, ensuring the stability and reliability of high-scale production workloads.
• Coordinate with internal stakeholders to ensure effective communication, issue resolution, and on-time delivery of projects.
• Participate in weekend and weekday on call rotation.

• Design, develop, and maintain scalable cloud-native data pipelines and microservices (GCP preferred; Azure/AWS acceptable)
• Build and optimize data ingestion, integration, and processing frameworks across diverse structured and unstructured data sources
• Develop Python-based APIs (Flask/ FastAPI ) to enable seamless data access, model integration, and service orchestration
• Implement advanced SQL-based analytics and transformations in BigQuery or other cloud data warehouse platforms
• Architect, deploy, and manage AI/agentic workflows to support intelligent automation and decisioning
• Orchestrate AI-driven pipelines integrating LLMs, agents, and data services for end-to-end workflow automation
• Drive automation and continuous improvement of data operations using AI-powered orchestration and monitoring
• Collaborate with cross-functional teams (Data Science, ML, and Product) to deliver scalable, secure, and production-grade AI-enabled data solutions

• Design, build and test end to end data pipeline including data ingestion (streaming, events, and batch), data integration, data curation
• Design, develop, and deploy scalable data pipelines and ETL processes on cloud-based infrastructure using Azure, Snowflake, DBT, Airflow, Cosmos DB
• Define and implement automation of jobs and testing
• Optimize the data pipeline to support workloads and use cases
• Support mission critical applications and near real time data needs from the data platform
• Address data issues, environment issues, performing impact analysis, root cause analysis, corrective, adaptive and perfective maintenance
• Implement data models, transformations, and schema designs to support analytical and reporting needs
• Optimize and tune Snowflake performance, including query optimization and resource management

Senior Snowflake Data Engineer

• Design, build, and maintain scalable data pipelines that support continuous data flow, analytics, and machine learning use cases
• Develop and implement data models and schemas to ensure data integrity, accessibility, and usability
• Manage and optimize relational and NoSQL database systems for performance and availability
• Integrate data from multiple sources while ensuring consistency, quality, and reliability
• Design and manage ETL processes to move and transform data across systems
• Implement data governance practices to support data security, privacy, and compliance
• Collaborate with data scientists, machine learning engineers, analysts, platform engineers, and business stakeholders to understand requirements and deliver effective solutions
• Tune database and pipeline performance for efficient processing and querying of large datasets
• Maintain clear documentation for data processes, models, architecture, and operational workflows
• Partner with internal platform teams to build reliable, scalable, and reusable data solutions

• Design, document, and maintain Axiom's enterprise data model, ensuring coherence and consistency across disparate systems and platforms.
• Establish and enforce data governance policies, data quality standards, and associated assurance mechanisms to ensure the reliability and integrity of organizational data assets.
• In coordination with the VP, Enterprise Applications, define and maintain the data architecture roadmap, including effort scoping, dependency identification, and alignment with enterprise technology strategy.
• Directly manage the Data Engineer; provide technical mentorship, set priorities, and support professional development.
• Serve as the primary data architecture liaison to the Product, Operations, and Data Science functions.
• In coordination with the VP, Enterprise Applications, evaluate and determine the technology stack for AI solutions and other data initiatives.
• Collaborate with the VP, Enterprise Applications and Integrations Lead to reconcile roadmap priorities and ensure integration-produced data meets enterprise data model standards.
• Participate collaboratively in the design and implementation of Model Context Protocol integrations, enabling AI tooling to access Axiom's information assets.
• Administer and govern the Databricks environment in coordination with the Data Engineer.
• Establish and maintain data documentation standards: entity definitions, lineage, ownership, and access policies.

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