Head of Data Engineering

Cadre AI is looking for a talented AI Engineer to join our dynamic team and help drive the development and optimization of our AI-powered platforms. This role requires strong technical expertise in machine learning, artificial intelligence, and large language models (LLMs), with a proven track record of building scalable AI solutions. The ideal candidate has experience working with OpenAI’s GPT series, Anthropic’s Claude, Google’s Gemini, and open-source models like Llama.

Role Overview

We are looking for a Head of Data Engineering to build and lead Cadre AI’s Data Engineering practice from the ground up. This is a senior, hands-on leadership role responsible for architecting and scaling multi-tenant Snowflake data warehouse solutions—both for internal operations and as a core service offering for our clients.

You will serve as the technical authority on all things data infrastructure, working directly with clients to design cloud-native data platforms, build modern ELT/ETL pipelines, and establish data governance frameworks. As the practice grows, you will recruit and mentor a team of data engineers, evolving this function into a standalone revenue-generating practice within Cadre AI.

This role is ideal for someone who thrives at the intersection of deep technical execution and client-facing consulting—someone who can whiteboard a Snowflake architecture in the morning, pair with engineers on dbt models in the afternoon, and present a data strategy roadmap to a client’s leadership team by end of day.

Data Architecture & Engineering

• Design, build, and optimize multi-tenant Snowflake data warehouse architectures for Cadre AI and its clients, ensuring scalability, security, and cost efficiency.
• Develop and maintain modern ELT/ETL pipelines using tools such as dbt, Airflow, Fivetran, and custom Python-based ingestion frameworks.
• Implement data modeling best practices (star schema, snowflake schema, Data Vault) tailored to each client’s analytical and operational needs.
• Establish data governance, quality, and lineage frameworks across multi-client environments.
• Drive cloud infrastructure decisions on AWS, Azure, or GCP with a focus on Snowflake-native capabilities including Snowpark, Cortex, Streamlit, and Snowpipe.
• Build repeatable reference architectures, accelerators, and templates that can be deployed across client engagements to improve delivery speed and consistency.

Client Delivery & Consulting

• Serve as the senior technical advisor and trusted consultant to clients on data strategy, architecture, and implementation.
• Lead discovery sessions, technical assessments, and data maturity evaluations for prospective and current clients.
• Translate complex business requirements into scalable data solutions and present technical roadmaps to executive stakeholders.
• Provide executive oversight on multi-client data engineering programs, ensuring projects are delivered on time, within scope, and at high quality.
• Support pre-sales efforts including scoping, estimation, proposal development, and technical solution design.

Practice & Team Building

• Build and scale the Data Engineering practice from the ground up—define the service offering, pricing model, delivery methodology, and team structure.
• Recruit, hire, and mentor data engineers as the practice grows, establishing a high-performance team culture grounded in technical excellence and client service.
• Develop and maintain internal knowledge bases, playbooks, and training materials for data engineering best practices.
• Collaborate with Cadre AI’s pod leads, AI engineers, and solutions architects to integrate data engineering into broader AI transformation engagements.
• Track practice KPIs including utilization, revenue, client satisfaction, and delivery quality.

Thought Leadership & Ecosystem

• Represent Cadre AI as a subject matter expert in the Snowflake and modern data stack ecosystem.
• Build and maintain relationships with Snowflake account executives, partner managers, and solution engineers.
• Contribute to Cadre AI’s brand through blog posts, conference talks, community engagement, and technical content.
• Stay current on emerging data technologies and evaluate their applicability for client solutions (e.g., Databricks, Microsoft Fabric, BigQuery, Iceberg/Delta Lake).

What You Need To Succeed

• 8+ years of professional experience in data engineering, data architecture, or a related technical role.
• 3+ years of hands-on experience with Snowflake as a primary data platform, including advanced features (Snowpark, Snowpipe, Tasks, Streams, Dynamic Tables).
• 3+ years in a client-facing consulting, professional services, or agency environment.
• Deep expertise in SQL, Python, and modern data transformation tools (dbt strongly preferred).
• Strong experience with cloud platforms (AWS preferred; Azure and GCP also valued) including infrastructure-as-code tools like Terraform.
• Proven experience designing multi-tenant data architectures with robust access control, data isolation, and cost allocation.
• Experience with data pipeline orchestration tools such as Airflow, Dagster, Prefect, or Databricks Workflows.
• Demonstrated ability to lead technical teams and grow a practice or function from early stage.
• Exceptional communication skills—ability to present to C-suite executives and collaborate with junior engineers equally effectively.
• Strong understanding of data governance, data quality frameworks, and regulatory compliance (SOC 2, GDPR, CCPA).
• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field (or equivalent experience).

Preferred Qualifications

• Snowflake SnowPro Advanced certifications (Architect, Data Engineer).
• Experience with Snowflake Cortex AI, Streamlit in Snowflake, and AI/ML data preparation workflows.
• Familiarity with complementary platforms: Databricks, Microsoft Fabric, Redshift, BigQuery.
• Experience building data products or analytics-as-a-service offerings for external customers.
• Background in industries Cadre AI serves: mortgage/financial services, IoT, SaaS, or professional services.
• Experience with real-time data pipelines (Kafka, Kinesis) and streaming architectures.
• Track record of contributing to the data community through speaking engagements, open-source contributions, or published content.
• Experience managing P&L responsibility or practice-level financial metrics in a consulting environment.

*Why Cadre AI*

• Ground-floor opportunity to build and own an entire practice within a rapidly scaling AI firm.
• Direct access to leadership—work alongside the co-founders and shape company strategy.
• Diverse, high-impact client engagements across industries with real AI transformation problems to solve.
• Pod-based team culture that values autonomy, ownership, and craft.
• Competitive compensation with performance-based upside as the practice scales.

If you're passionate about advancing AI and eager to make an impact, we’d love to hear from you! Join Cadre AI and contribute to shaping the future of AI innovation.

Compensation

The base pay range for this role is $120,000 – $160,000 per year.