Senior Director, Data Platform & Engineering

Caliber Collision•Lewisville, TX

About The Position

We are seeking an experienced and innovative Data Platform & Tools Leader to design, operate, and optimize Caliber’s enterprise data and analytics ecosystem. This is an outstanding opportunity to join a fast-moving, innovative team that enjoys strong partnerships across the business—our technology is in high demand, and business stakeholders are eager to leverage our solutions for strategic impact. We’re looking for a true go-getter who not only understands the business value of data but also knows how to create and deliver the platforms that unlock its full potential. This role will lead the strategy, architecture, and management of the company’s data platform—ensuring that data is reliable, scalable, secure, and AI-ready to power business insights and transformation across all business functions, including operations, finance, HR, supply chain, and customer experience. Reporting to the Vice President, Data, Analytics & AI, the Data Platform & Tools Leader will collaborate closely with data engineering, analytics, governance, IT, and business teams to deliver a unified and modern data foundation that enables self-service analytics and enterprise AI initiatives.

Requirements

Data Platform Expertise: Deep experience with cloud data platforms (e.g., Snowflake, Synapse, Databricks, Redshift, or BigQuery) and supporting tools (e.g., dbt, Airflow, Fivetran).
Cloud & Infrastructure Knowledge: Strong understanding of cloud architecture (Azure or AWS), networking, compute optimization, and cost management.
DataOps & Automation: Hands-on experience implementing CI/CD, observability, and infrastructure-as-code practices (Terraform, GitHub Actions, or Azure DevOps).
BI & Analytics Tools: Familiarity with Power BI, Tableau, or similar tools, including security, performance tuning, and semantic model management.
Security & Compliance: Knowledge of data privacy frameworks (GDPR, CCPA, SOX) and integration of governance policies into platform design.
Data Architecture & Modeling: Experience designing conceptual, logical, and physical models; strong SQL and schema design skills.
Analytical & Technical Acumen: Ability to translate business requirements into scalable technical solutions that enable insight generation and AI readiness.
Communication & Collaboration: Strong interpersonal and communication skills to work effectively with IT, business leaders, and cross-functional teams.
Continuous Improvement Mindset: Proven ability to modernize processes, automate workflows, and improve data platform performance and trust.
Education: Bachelor’s degree in computer science, data engineering, information systems, or a related field; a master’s degree is a plus.
Experience: Minimum of 10 years of experience in data engineering, cloud data platform administration, or analytics infrastructure leadership, including 3+ years in a lead or architect-level role.

Nice To Haves

Certifications ( preferred – one or more relevant certification):
SnowPro Core / Advanced Architect
Microsoft Azure Data Engineer / Solutions Architect
AWS Certified Data Analytics or equivalent
dbt Fundamentals or Practitioner certification
ITIL, CISSP, or other governance/security certifications are a plus

Responsibilities

Platform Strategy & Operations: Lead the design, build, and operation of the enterprise data platform, including cloud data warehouse, data lake, integration, and orchestration layers.
Tool Ownership: Administer and optimize analytics and data tools (e.g., Snowflake, Azure, Databricks, Power BI, dbt) for usability, performance, reliability, cost efficiency, and scalability.
Data Architecture & Modernization: Drive modernization of legacy systems into a cloud-native, governed, and high-performance environment that supports AI and analytics at scale.
DataOps & Automation: Implement CI/CD pipelines, data observability, version control, and automated testing to ensure reliable, repeatable, and auditable data delivery.
Security & Compliance: Partner with Data Governance, Cybersecurity, and IT GRC to ensure access control, data encryption, privacy, and compliance with internal and external regulations.
Cross-Functional Collaboration: Work with Analytics CoE, Data Governance, and AI CoE to ensure seamless integration between data pipelines, models, and business-facing insights.
Performance Optimization: Monitor and tune system performance to balance compute and storage efficiency while maintaining data freshness and reliability.
Innovation Enablement: Prepare platform capabilities for enterprise AI adoption, including support for APIs, vector stores, and LLM data integrations.
Documentation & Standards: Establish platform standards, data pipeline documentation, and operational runbooks for consistency and scalability.
Mentorship & Team Leadership: Build and develop a high-performing team of platform engineers, administrators, and DataOps professionals focused on operational excellence and innovation.