Data Engineer

TDIndustries is seeking a hands-on Data Engineer to support the build-out of its data platform. The role is responsible for designing and delivering production-grade data pipelines, lakehouse architecture, data models, and data products using Snowflake, Microsoft Fabric, and dbt, while supporting AI and ML workloads and contributing to engineering standards and team development.

Responsibilities

• Design, build, and maintain production - grade data pipelines that ingest, transform, and deliver data across the medallion architecture (Bronze / Silver / Gold) on Snowflake and Microsoft Fabric
• Implement ELT/ETL patterns using dbt as the primary transformation layer, writing modular, tested, well - documented models that serve as the foundation for data products
• Develop and maintain data pipelines supporting batch, micro - batch, and streaming ingestion across enterprise source systems - including Dynamics 365, Procore, Workday, IoT, and field operations platforms
• Translate solution architectures designed by the Sr. Solution Architect into engineered, production - ready implementations - bridging the gap between architecture intent and delivery reality
• Own pipeline reliability - monitor, alert on, and remediate data pipeline failures proactively, ensuring data products meet their SLAs
• Build and maintain data models within the lakehouse platform, applying Kimball dimensional modeling, Data Vault, or OBT patterns as appropriate for the use case and consumption layer
• Implement and enforce medallion architecture standards across the data platform, ensuring Bronze, Silver, and Gold layers are clearly defined, properly governed, and fit - for - purpose
• Develop reusable data assets - conformed dimensions, shared fact tables, and domain - specific data products - that serve multiple analytics and AI consumers without duplication
• Contribute to data product design, providing engineering input on schema, SLAs, lineage, and consumption interfaces alongside Data Product Managers and the Solution Architect
• Build and maintain reusable engineering frameworks, templates, and patterns - for pipeline design, dbt project structure, testing strategy, and deployment - that bring standardization and speed to the team's delivery cadence
• Define and enforce data engineering best practices: code review standards, branching strategies, testing requirements, documentation expectations, and deployment patterns
• Establish and maintain data quality tests within dbt and the broader pipeline stack, ensuring data products are validated before they reach analytics and AI consumers
• Contribute to architecture decision records (ADRs) and technical documentation that build institutional knowledge and enable the team to scale without key - person dependency
• Build and maintain data pipelines that support AI and ML workloads - including feature engineering pipelines, embedding generation pipelines, and data feeds for RAG architectures on Snowflake Cortex
• Stay current with the evolving intersection of data engineering and AI - driven platforms - Snowflake Cortex, Microsoft Fabric's AI capabilities, dbt Semantic Layer - and bring grounded, practical recommendations to the team
• Evaluate emerging tools and patterns as the data - driven AI landscape evolves, recommending best - in - class options based on fit with TD's platform and requirements rather than novelty
• Guide and upskill team members on Snowflake - based data engineering - sharing knowledge on performance optimization, data modeling patterns, cost management, and pipeline design
• Actively participate in design and code reviews, providing constructive, evidence - based feedback that raises the technical quality of the team's output
• Contribute to hiring and technical assessment as the data engineering team scales, helping identify candidates with the depth and mindset the team needs
• Model engineering habits that the team should adopt - clean code, strong testing, clear documentation, and a continuous improvement mindset
• Perform other duties as required

Preferred

• Experience with AI - adjacent data engineering (feature pipelines, Cortex, embeddings) is a significant advantage
• Experience in construction, building services, facilities management, or a similarly operationally complex industry is a plus - familiarity with project - based business data is directly relevant
• Familiarity with feature engineering patterns, embedding pipelines, and vector store integration for RAG - based AI workloads is a meaningful advantage
• AI Data Engineering
• Feature Pipelines
• Cortex
• Embeddings
• Construction Industry
• Building Services
• Facilities Management
• Operationally Complex Industries
• Project-Based Business Data
• Feature Engineering
• Embedding Pipelines
• Vector Store Integration
• RAG-Based AI Workloads

Company

• Mechanical construction and facility services
• Dallas, Texas, USA
• https://www.tdindustries.com/
• Founded in 1946
• 1001-5000 employees

Qualifications

• 8-10 years of hands - on data engineering experience, with demonstrated depth in Snowflake and modern ELT/ETL practices - this is a firm floor; candidates without this will not be considered
• Production - grade dbt experience - not proof - of - concept or side - project exposure, but experience building and maintaining dbt projects in enterprise environments
• Demonstrated experience building data products on a lakehouse architecture, applying medallion architecture patterns at production scale
• Proven track record of building reusable engineering frameworks, pipeline templates, or standards that improved team delivery speed and consistency
• Experience working in environments with multiple enterprise source systems (ERP, CRM, HCM, or field operations platforms) and complex integration patterns
• Evidence of mentoring or upskilling peers - formal or informal; this role is expected to grow into a technical lead
• Business context awareness - understands that pipelines serve business decisions; builds with the consumer's needs in mind, not just technical correctness
• Engineering craftsmanship - writes clean, tested, well - documented code; treats data pipelines as production software, not scripts
• Standards - driven - instinctively builds for reuse and repeatability; resists one - off solutions when a pattern would serve the team better
• Adaptability - tracks where the data engineering discipline is heading, especially as AI capabilities reshape what pipelines need to do, and adapts without chasing hype
• Collaborative depth - partners effectively with architects, product managers, and governance leads; not a siloed executor
• Constructive ownership - identifies problems early, surfaces them clearly, and drives resolution rather than waiting for direction
• Growth orientation - energized by the opportunity to grow into technical leadership as the DAI function scales
• Snowflake - data modeling, Snowpark, dynamic tables, streams and tasks, data sharing, query optimization, cost management, and security configuration. This is a firm requirement; candidates without deep hands - on Snowflake experience will not be considered
• Microsoft Fabric - Fabric Lakehouse, OneLake, Data Warehouse, Dataflows Gen2, Data Factory pipelines, Fabric notebooks, and Eventstream; working proficiency required
• dbt - model design, modular project structure, testing (schema, data, custom), documentation, incremental strategies, and dbt Semantic Layer; deep proficiency required
• Power BI - understanding of semantic model design and how downstream BI consumption shapes upstream data modeling decisions
• Python - data pipeline scripting, Snowpark development, and automation; strong proficiency required
• SQL - advanced query writing, window functions, CTEs, performance tuning; expert - level proficiency required
• Azure ecosystem - Azure Data Factory, Azure Event Hubs, Azure Blob Storage, and Azure Key Vault; working proficiency in the context of data platform integration
• Git and CI/CD - version control discipline, branching strategies, and deployment automation for data pipeline code
• Snowflake Cortex - Cortex Analyst, Cortex Semantic Views, and Snowflake ML Functions for AI - grounded data pipelines
• Awareness of how the dbt Semantic Layer, Snowflake Horizon, and Microsoft Fabric's AI services intersect with data engineering delivery
• Dbt - model design, modular project structure, testing (schema, data, custom), documentation, incremental strategies, and dbt Semantic Layer; deep proficiency required
• Experience with AI - adjacent data engineering (feature pipelines, Cortex, embeddings) is a significant advantage
• Experience in construction, building services, facilities management, or a similarly operationally complex industry is a plus - familiarity with project - based business data is directly relevant
• Familiarity with feature engineering patterns, embedding pipelines, and vector store integration for RAG - based AI workloads is a meaningful advantage
• 8-10 years of hands - on data engineering experience, with demonstrated depth in Snowflake and modern ELT/ETL practices - this is a firm floor; candidates without this will not be considered.

Required skills

• Azure Blob Storage, Azure Data Factory, Azure Event Hubs, Azure Key Vault, CI/CD, Data Modeling, Data Pipeline Engineering, Feature Engineering, Git, Lakehouse Architecture, Medallion Architecture, Microsoft Fabric, Power BI, Python, SQL, Snowflake, Snowflake Cortex, dbt

Preferred skills

• (1), (2), *2026, 100, 2020, 2023, 2025-12-22, 2026-03-04, 2026-03-31, AI Tools, Analyze How Well You Fit, Andrew Stokes, Build Cover Letter, Chief Financial Officer, Company data provided by, Customize Your Resume, Dallas, Texas, USA, Data Powered by, Distribution of Different Job Fields Receiving Sponsorship, Engineering and Development, H1B Sponsorship, Late Stage, Leadership Team, Make your application stand out, Maximize your interview chances, PR Newswire, Recent News, Represents job field similar to this job, Trends of Total Sponsorships, https://www.tdindustries.com/

Experience

• 8-10 years