Principal Data Engineer – AI/RAG Data Pipelines, Snowflake, dbt, Python & SQL — Detroit

Principal Data Engineer – AI-Ready Data | Snowflake | dbt | Python | SQL

Location: Detroit / Southeast Michigan – Primarily Remote; onsite collaboration 2–4 times per month, up to 1 day per week.

Compensation: $174,000–$190,000/year, plus a 15% target annual bonus and RSUs targeted at 60% of annualized base salary.

Position Summary

The Principal Data Engineer will own the architecture and hands-on engineering of the company's enterprise data platform, with a primary focus on making enterprise data AI-ready, accessible, trusted, and usable across the organization.

This individual will assess the current data environment, identify architectural and data-quality gaps, define the future-state architecture and roadmap, and personally engineer and implement the solutions required to create a scalable, governed data foundation that supports analytics, reporting, AI/ML, and autonomous AI-agent use cases.

This is a senior individual contributor role combining enterprise data architecture with hands-on data engineering. The ideal candidate will bring an extensive background in enterprise databases, data warehousing, data modeling, ETL/ELT pipelines, and modern data architecture, along with experience preparing enterprise data for AI applications.

The current environment includes Snowflake, Fivetran, dbt, Astronomer/Airflow, Python, and SQL. The company is also open to experience with Databricks and comparable data integration and orchestration technologies. The emphasis is on deep architectural and engineering expertise, rather than experience with every tool in the existing environment.

The role will own the full data lifecycle, including source-system integration, ingestion, orchestration, transformation, data modeling, data quality, metadata and data dictionaries, governance, security and access controls, and curated enterprise data layers. This includes evaluating approaches such as knowledge graphs, vectorization, and embeddings to make enterprise data more accessible and useful for AI applications.

When larger initiatives require additional implementation capacity, external resources may be utilized, but this role retains direct architectural and engineering ownership of the enterprise data platform.

Key Responsibilities

Own the architecture, hands-on engineering, technical direction, and roadmap for the enterprise data platform, with a primary focus on making enterprise data AI-ready and accessible.

Conduct a comprehensive current-state assessment of the existing data environment, identifying architectural, engineering, data quality, governance, security, scalability, and operational gaps.

Define the future-state data architecture and develop a practical roadmap for evolving the current environment.

Personally architect, engineer, implement, and support enterprise data solutions using Snowflake and/or comparable enterprise data platforms, including Databricks, along with dbt, Python, SQL, and appropriate integration and orchestration technologies.

Design and develop scalable data ingestion, ETL/ELT pipelines, orchestration, transformation, data models, warehousing, and curated enterprise data layers.

Design and implement data architectures that make enterprise information accessible and usable by AI/ML applications, autonomous AI agents, analytics, and reporting systems.

Evaluate and implement AI-ready data approaches, including knowledge graphs, vectorization, embeddings, and retrieval-oriented data structures, as appropriate to business requirements.

Establish and implement modern data architecture principles, engineering standards, design patterns, and development best practices.

Evaluate the existing technology stack and determine where technologies should be retained, optimized, replaced, or introduced based on technical requirements, scalability, maintainability, security, cost, and business needs.

Establish and implement data quality standards and controls, including validation, testing, monitoring, reconciliation, and processes for maintaining accurate, consistent, trusted enterprise data.

Establish and maintain data governance practices, including metadata management, data dictionaries, lineage, ownership, classification, documentation, and standards for governed and reusable enterprise datasets.

Design and implement appropriate data security and access controls, including role-based access and protection of sensitive or restricted enterprise information.

Integrate data from enterprise applications and source systems, including Salesforce, contract-management platforms, financial systems, and other business applications, into the enterprise data platform.

Address complex legacy data challenges resulting from decades of historical data, multiple migrations, acquisitions, disparate source systems, and inconsistent historical standards.

Support the integration and curation of contract and other enterprise data within the existing Snowflake environment, including data originating from platforms such as Agiloft.

Create trusted, structured, appropriately governed data layers that can be reliably consumed by analytics, reporting, AI/ML, and autonomous AI agents.

Partner directly with business and technical stakeholders to understand enterprise data requirements and translate them into scalable technical solutions.

Evaluate new technologies and architectural approaches through technical analysis, proofs of concept, tradeoff analysis, and business requirements rather than simply maintaining the existing technology stack.

Direct external resources or contractors when additional implementation capacity is required while retaining direct ownership of architecture, technical decisions, engineering, and delivery. </u