The Data Engineer is a hands - on technical role at the delivery core of TDIndustries' data platform build - out. This is not a support or maintenance role - it is an engineering role with direct accountability for building the data pipelines, lakehouse architecture, and data products that the business depends on for decisions, analytics, and AI capabilities.

The right candidate brings deep, practical expertise in Snowflake, Microsoft Fabric, and dbt, and applies that expertise to design and deliver pipelines and data products that are performant, well - governed, and built to last. They understand how to translate architecture from blueprint to production, write code that others can maintain, and build frameworks and standards that make the team faster over time. They are curious about where data engineering is heading - particularly the intersection of data pipelines and AI - driven workloads - and bring that curiosity back to the team in practical ways.

This is a Mid - Senior Individual Contributor (Hands - On) role that works closely with the Sr. Solution Architect and Data Product Managers, contributing meaningfully to architectural decisions while owning engineering execution. It is the right role for someone who wants to grow into a technical lead and eventually take ownership of the data engineering capability as the DAI function scales.

Data Pipeline Engineering & Delivery

• 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.

Lakehouse Architecture & Data Modeling

• 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.Standards, Frameworks & Engineering Excellence
• 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.

Data - Driven AI Pipeline Support

• 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.Team Capability & Mentorship
• 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.

• 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.
• 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.
• 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.

Core Platform (Required - In - Depth, Hands - On Proficiency)

• 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.

Data Engineering Stack

• 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.

Data - Driven AI & Emerging Capabilities

• Snowflake Cortex - Cortex Analyst, Cortex Semantic Views, and Snowflake ML Functions for AI - grounded data pipelines.
• Familiarity with feature engineering patterns, embedding pipelines, and vector store integration for RAG - based AI workloads is a meaningful advantage.
• Awareness of how the dbt Semantic Layer, Snowflake Horizon, and Microsoft Fabric's AI services intersect with data engineering delivery.