Lead assessment and redesign of a legacy data ecosystem into a scalable data lake/lakehouse. Define target-state architecture, canonical models, governance, validation, lineage, and a phased migration roadmap to stabilize downstream batch processing and enable partner onboarding.
We are seeking a Senior Data Modeler to lead the assessment and redesign of a legacy data ecosystem into a modern, scalable data lake architecture. This role will define the target-state data architecture and operating model across ingestion, standardization, validation, curated datasets, and governance in a complex, multi-partner environment.
The engagement will culminate in a structured gap analysis and phased migration roadmap, with clear sequencing and implementation recommendations. The ideal candidate brings deep modeling expertise, architectural clarity, and the ability to stabilize downstream processing ecosystems through disciplined data design and governance.
Responsibilities
- Assess existing data ingestion pipelines, storage structures, data models, and batch processing dependencies
- Identify root causes of recurring data quality issues, lineage gaps, and downstream performance bottlenecks
- Design a scalable, layered data architecture supporting raw ingestion, standardized data, and curated consumer-aligned datasets
- Define canonical data models to standardize heterogeneous partner data across formats
- Recommend and apply appropriate modeling approaches, including dimensional, Data Vault, normalized, or hybrid models
- Establish data validation, enrichment, and transformation patterns that improve reliability and consistency
- Define data quality rules, validation checkpoints, naming conventions, and metadata standards
- Design data contracts and schema evolution strategies to support partners onboarding and backward compatibility
- Establish governance and ownership models, including data stewardship and accountability frameworks
- Define metadata management, lineage tracking, observability, and lifecycle management standards
- Conduct structured gap analysis between current and target states
- Develop a phased migration roadmap that minimizes operational risk and stabilizes downstream batch ecosystems
- Provide implementation recommendations across people, process, tooling, and governance
Required Experience
- Advanced expertise in logical and physical data modeling across structured and semi-structured data
- Strong experience designing canonical data models and standardization strategies
- Deep understanding of dimensional modeling, including star and snowflake schemas
- Practical knowledge of Data Vault and hybrid modeling approaches
- Experience transforming legacy data platforms into modern data lake or lakehouse ecosystems
- Proven track record addressing systemic data quality issues at scale
- Experience designing ingestion frameworks for heterogeneous data sources
- Strong grasp of metadata management, lineage, and observability concepts
- Experience defining governance models and stewardship frameworks
- Demonstrated ability to conduct structured assessments, gap analysis, and roadmap development
- Strong stakeholder engagement and architectural communication skills
Preferred Experience
- Experience leading enterprise-scale data modernization initiatives
- Demonstrated success in stabilizing downstream batch ecosystems through improved modeling and governance
- Experience delivering phased migration roadmaps in complex, multi-partner data environments
- Experience supporting healthcare payer data domains such as claims, enrollment, providers, or quality reporting
McLaren Strategic Solutions is a leading-edge global technology consulting firm, addressing critical challenges across industries such as retail, financial services, and healthcare. Integrating a powerful ecosystem of platforms with capital-efficient execution, McLaren specializes in digital transformation to help businesses optimize operations, accelerate revenue, and achieve scalable outcomes. McLaren’s expertise spans the development of customer-centric applications, modernizing systems for cost-effectiveness and security, and leveraging cloud scalability for future-ready architectures. With a deep commitment to operational excellence, McLaren provides comprehensive managed services, including application maintenance, cybersecurity, platform solutions, and AI-optimized operations, ensuring seamless, secure, and efficient performance.
From supply chain automation to compliance and analytics, McLaren drives measurable impact: improving workforce productivity, reducing inventory costs, and cutting technology ownership expenses. With its emphasis on automation and zero business downtime, McLaren facilitates seamless migrations from legacy systems to modern platforms, enabling organizations to harness the full potential of digital transformation. Backed by strategic partnerships and a proven delivery model, McLaren empowers clients to innovate, modernize, and achieve lasting success in today’s digital economy.
McLaren is a certified minority owned business through the NMSDC and has a mission to place more people from non-traditional backgrounds into sustainable technology careers. Through partnerships with non-profit technology programs in underserved communities and Veteran organizations, candidates transition from tech training programs into real IT careers at McLaren. Our unique recruitment policy allows us to create exceptional teams, bringing a broad spectrum of experience to our company and creating anything but a traditional consulting firm.
Visit McLaren Strategic Solutions to learn more!
Doran Jones Inc. is proud to be part of the McLaren Strategic Ventures Group.
Top Skills
Canonical Data Models
Data Contracts
Data Governance
Data Ingestion Pipelines
Data Lake
Data Lineage
Data Stewardship
Data Vault
Dimensional Modeling
ETL
Lakehouse
Metadata Management
Observability
Schema Evolution
Snowflake Schema
Star Schema
Similar Jobs
Automotive • Insurance
Lead enterprise conceptual, logical, and physical data modeling for P&C and life insurance domains. Design models for ODS, data lakes/warehouses, and streaming pipelines; enforce governance, metadata, and MDM alignment; collaborate with engineers, architects, and business SMEs; mentor junior modelers and produce documentation to support scalable, analytics-ready data platforms.
Top Skills:
Api Data ContractsAWSAzure Data Modeling ToolsData Vault 2.0DatabricksEr/StudioErwinEvent-Driven ModelingKafkaKinesisMdmSnowflakeSQL
.jpeg)
Artificial Intelligence • Hardware • Information Technology • Machine Learning
Responsible for defining and integrating Design for Test (DFT) architectures for High Bandwidth Memory (HBM) products, ensuring robust test coverage while collaborating across multiple teams to deliver quality solutions.
Top Skills:
Computer EngineeringDft ArchitecturesElectrical EngineeringRtl DesignSystemverilogVhdl
Artificial Intelligence • Hardware • Information Technology • Machine Learning
Define micro-architecture for digital subsystems, implement RTL builds in SystemVerilog, collaborate with DV teams, and support validation and debugging processes.
Top Skills:
Systemverilog
What you need to know about the Chicago Tech Scene
With vibrant neighborhoods, great food and more affordable housing than either coast, Chicago might be the most liveable major tech hub. It is the birthplace of modern commodities and futures trading, a national hub for logistics and commerce, and home to the American Medical Association and the American Bar Association. This diverse blend of industry influences has helped Chicago emerge as a major player in verticals like fintech, biotechnology, legal tech, e-commerce and logistics technology. It’s also a major hiring center for tech companies on both coasts.
Key Facts About Chicago Tech
- Number of Tech Workers: 245,800; 5.2% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: McDonald’s, John Deere, Boeing, Morningstar
- Key Industries: Artificial intelligence, biotechnology, fintech, software, logistics technology
- Funding Landscape: $2.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Pritzker Group Venture Capital, Arch Venture Partners, MATH Venture Partners, Jump Capital, Hyde Park Venture Partners
- Research Centers and Universities: Northwestern University, University of Chicago, University of Illinois Urbana-Champaign, Illinois Institute of Technology, Argonne National Laboratory, Fermi National Accelerator Laboratory
