Mid Level Lead Data Architect Jobs
Mid level lead data architect jobs go to professionals ready to own end-to-end data architecture decisions, drive schema and pipeline design with minimal oversight, and guide junior team members through complex builds. Hiring is active across Technology & Software, Consulting & Professional Services, and Staffing & Recruiting, with 39% remote or hybrid availability, and employers like HDR, TikTok, and Ursus competing for architects at this level now.
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Showing 5 of 35+ Mid Level Lead Data Architect jobs
Key Responsibilities:
- ELT Pipeline Engineering & Automation.
- Pipeline Orchestration: Designs and maintains programmatic, automated ELT pipelines (i.e. SQL, Python, or modern cloud data tools) to process data incrementally through the Medallion stages.
- Performance Tuning: Conducts query and cluster tuning for modern cloud data platforms, ensuring highly optimized compute usage during heavy data transformations and processing.
-
Data Migration Execution: Writes robust code to safely migrate legacy databases and files into automated Lakehouse ingestion pipelines.
-
Advanced Data Modeling & Cloud Data Architecture.
- Lakehouse Strategy: Works with IT Architecture staff to design and implement a scalable, secure Medallion Data Architecture across cloud environments.
- Layer Optimization: Establishes standards, schemas, and file formats (i.e. Iceberg, Parquet) for ingestion and data lake processing.
- Analytical Modeling: Designs highly efficient physical and logical data structures within the Gold layer, including modern dimensions, facts, and business entity schemas.
- Schema Evolution: Builds resilient data models capable of handling semi-structured data (JSON, API payloads) alongside structured data, maintaining robust version control of data contracts.
-
Semantic Layer Design: Transforms complex business reporting requirements into user-friendly semantic layers that support self-service business intelligence.
-
Develop and document standards/best practices.
-
Creates documentation of pipelines, architecture, modeling, and migration efforts.
-
Data migrations, business process monitoring.
-
Designs standards for the exchange of data between systems, including data security models.
-
Reporting business intelligence.
- Understands business intelligence data needs.
- Defines the data warehouse/business intelligence design.
- Implements data warehouses and data marts.
- Implements business intelligence tools and user interfaces.
- Implements enterprise reporting.
- Implements management dashboards and scorecards.
- Implements analytic applications.
-
Replicates and transforms data for business intelligence.
-
Modern Data Governance, Metadata Management & Cataloging.
- Metadata Integration: Manages and curates the Alation Data Catalog, ensuring all tables, schemas, and views across the Bronze, Silver, and Gold layers are thoroughly indexed, classified, and tagged.
- Lakehouse Data Lineage: Maps and monitors complex, end-to-end data lineage from source applications down to Gold-layer reporting assets within Alation.
-
Data Quality Frameworks: Designs programmatic data validation and testing protocols at the boundary between the Bronze and Silver layers to enforce strict enterprise data stewardship.
-
Mentoring.
- Plans and conducts formal mentorship activities for peers and/or lower-level staff via verbal instruction or technical documentation.
Mandatory skills
- 5+ years of hands-on experience designing and implementing enterprise data warehouse, data lake, or Lakehouse solutions.
- Expert-level SQL skills, including query optimization, performance tuning, and development of complex analytical datasets.
- Experience developing automated ELT pipelines using Python, SQL, and modern cloud data platform technologies.
- Hands-on experience implementing and supporting Snowflake-based data engineering, data warehousing, and analytics solutions.
- Experience designing and managing Medallion Architecture (Bronze, Silver, Gold) data platforms and associated data processing standards.
- Advanced experience with dimensional data modeling, including fact tables, dimensions, star schemas, and semantic layer design.
- Experience integrating structured and semi-structured data sources, including APIs, JSON, and file-based ingestion frameworks.
- Hands-on experience administering enterprise data catalog and governance platforms such as Alation.
- Experience implementing data governance, metadata management, data lineage, and data quality frameworks.
- Experience executing data migration and modernization initiatives.
- Ability to define technical standards, best practices, and architecture documentation for enterprise data platforms.
- Ability to work independently and lead complex, cross-functional data architecture and engineering initiatives.
- Excellent communication skills and ability to collaborate with technical teams, business stakeholders, and executive leadership.
Certifications or Desired skills if any
- Experience with Snowflake cloud-native architecture, including Snowflake Warehouses, Snowpark, Streams, Tasks, and secure data sharing.
- Familiarity with data modeling (i.e. ER Studio) and modern modeling methodologies.
- Experience working in Agile, iterative, or rapidly evolving solution environments.
- Experience with enterprise data cataloging, metadata management, and data lineage tools (e.g., Alation).
Nice To Have Skills
- Strong work ethic/Time & attendance punctuality.
- Experience with dbt.
Preferred Education:
- Bachelor’s Degree in Data Analytics, Computer Science, Information Technology, Computer Engineering, or a closely related field.
Key Responsibilities:
- ELT Pipeline Engineering & Automation.
- Pipeline Orchestration: Designs and maintains programmatic, automated ELT pipelines (i.e. SQL, Python, or modern cloud data tools) to process data incrementally through the Medallion stages.
- Performance Tuning: Conducts query and cluster tuning for modern cloud data platforms, ensuring highly optimized compute usage during heavy data transformations and processing.
-
Data Migration Execution: Writes robust code to safely migrate legacy databases and files into automated Lakehouse ingestion pipelines.
-
Advanced Data Modeling & Cloud Data Architecture.
- Lakehouse Strategy: Works with IT Architecture staff to design and implement a scalable, secure Medallion Data Architecture across cloud environments.
- Layer Optimization: Establishes standards, schemas, and file formats (i.e. Iceberg, Parquet) for ingestion and data lake processing.
- Analytical Modeling: Designs highly efficient physical and logical data structures within the Gold layer, including modern dimensions, facts, and business entity schemas.
- Schema Evolution: Builds resilient data models capable of handling semi-structured data (JSON, API payloads) alongside structured data, maintaining robust version control of data contracts.
-
Semantic Layer Design: Transforms complex business reporting requirements into user-friendly semantic layers that support self-service business intelligence.
-
Develop and document standards/best practices.
-
Creates documentation of pipelines, architecture, modeling, and migration efforts.
-
Data migrations, business process monitoring.
-
Designs standards for the exchange of data between systems, including data security models.
-
Reporting business intelligence.
- Understands business intelligence data needs.
- Defines the data warehouse/business intelligence design.
- Implements data warehouses and data marts.
- Implements business intelligence tools and user interfaces.
- Implements enterprise reporting.
- Implements management dashboards and scorecards.
- Implements analytic applications.
-
Replicates and transforms data for business intelligence.
-
Modern Data Governance, Metadata Management & Cataloging.
- Metadata Integration: Manages and curates the Alation Data Catalog, ensuring all tables, schemas, and views across the Bronze, Silver, and Gold layers are thoroughly indexed, classified, and tagged.
- Lakehouse Data Lineage: Maps and monitors complex, end-to-end data lineage from source applications down to Gold-layer reporting assets within Alation.
-
Data Quality Frameworks: Designs programmatic data validation and testing protocols at the boundary between the Bronze and Silver layers to enforce strict enterprise data stewardship.
-
Mentoring.
- Plans and conducts formal mentorship activities for peers and/or lower-level staff via verbal instruction or technical documentation.
Mandatory skills
- 5+ years of hands-on experience designing and implementing enterprise data warehouse, data lake, or Lakehouse solutions.
- Expert-level SQL skills, including query optimization, performance tuning, and development of complex analytical datasets.
- Experience developing automated ELT pipelines using Python, SQL, and modern cloud data platform technologies.
- Hands-on experience implementing and supporting Snowflake-based data engineering, data warehousing, and analytics solutions.
- Experience designing and managing Medallion Architecture (Bronze, Silver, Gold) data platforms and associated data processing standards.
- Advanced experience with dimensional data modeling, including fact tables, dimensions, star schemas, and semantic layer design.
- Experience integrating structured and semi-structured data sources, including APIs, JSON, and file-based ingestion frameworks.
- Hands-on experience administering enterprise data catalog and governance platforms such as Alation.
- Experience implementing data governance, metadata management, data lineage, and data quality frameworks.
- Experience executing data migration and modernization initiatives.
- Ability to define technical standards, best practices, and architecture documentation for enterprise data platforms.
- Ability to work independently and lead complex, cross-functional data architecture and engineering initiatives.
- Excellent communication skills and ability to collaborate with technical teams, business stakeholders, and executive leadership.
Certifications or Desired skills if any
- Experience with Snowflake cloud-native architecture, including Snowflake Warehouses, Snowpark, Streams, Tasks, and secure data sharing.
- Familiarity with data modeling (i.e. ER Studio) and modern modeling methodologies.
- Experience working in Agile, iterative, or rapidly evolving solution environments.
- Experience with enterprise data cataloging, metadata management, and data lineage tools (e.g., Alation).
Nice To Have Skills
- Strong work ethic/Time & attendance punctuality.
- Experience with dbt.
Preferred Education:
- Bachelor’s Degree in Data Analytics, Computer Science, Information Technology, Computer Engineering, or a closely related field.
See All 35 Mid Level Lead Data Architect Jobs
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Find JobsMid Level Lead Data Architect Job Market
Who's Hiring
Top Industries Hiring
- Technology & Software14
- Consulting & Professional Services5
- Staffing & Recruiting4
- Healthcare & Medical Services3
- Science & Research2
Mid Level Lead Data Architect Jobs: Frequently Asked Questions
How do I get a mid level lead data architect job?
Position yourself around ownership, not just contribution. Highlight projects where you designed or restructured data models, made architectural tradeoffs independently, or led a component of a larger platform build. Emphasize hands-on work with cloud data warehouses, ETL pipelines, or data governance frameworks. Certifications in Snowflake, dbt, or a major cloud provider strengthen mid level applications considerably.
Which companies hire mid level lead data architects?
Companies hiring mid level lead data architects right now include HDR, TikTok, and Ursus, based on current listings on Migrate Mate as of November 2026. At this level, hiring tends to come from mid-size technology firms, enterprise companies modernizing legacy data infrastructure, and financial or healthcare organizations scaling their analytics platforms.
Are there remote mid level lead data architect jobs?
Yes, remote and hybrid options are well represented at this level. About 39% of mid level lead data architect openings are remote or hybrid as of November 2026, reflecting the cloud-native and documentation-heavy nature of architecture work that translates well to distributed teams.
How do I move up to a mid level lead data architect role?
Moving from entry level into mid level means shifting from executing tasks to owning outcomes. Build depth in a core stack, such as a cloud platform and a modeling layer like dbt or dimensional modeling. Take on progressively larger scopes, document the architectural decisions you make, and demonstrate that your work reduced pipeline failures, improved query performance, or supported a downstream product.
Which industries hire the most mid level lead data architects?
Mid Level lead data architect roles concentrate in Technology & Software, Consulting & Professional Services, and Staffing & Recruiting, based on current listings on Migrate Mate as of November 2026. These sectors generate high volumes of structured and semi-structured data that require dedicated architecture investment, making mid level architects a consistent hire as data platforms mature and scale.