Lead Architect, Databricks

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Lead Architect, Databricks

Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets. An ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better.

We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a "Cool Vendor" and a "Vendor to Watch" by Gartner.

Please visit Fractal | Intelligence for Imagination for more information about Fractal

Location: Dallas, Texas (Client onsite)

Note: This position is not eligible for Immigration Sponsorship currently.

Role Overview:

Fractal is seeking an experienced Lead Databricks Architect to drive the design, implementation, modernization, and governance of enterprise Data & AI platforms on Databricks for a leading global organization.

This is a client-facing architecture role for someone who combines deep technical expertise with strong stakeholder engagement skills. The Lead Databricks Architect will work closely with business stakeholders, product owners, data engineers, data scientists, enterprise architects, and cloud platform teams to build scalable, secure, and business-aligned data platforms and products.

The ideal candidate brings strong expertise in Databricks Lakehouse architecture, cloud-native data engineering, analytics, AI/ML enablement, and data governance. They will be responsible for defining solution architecture, establishing engineering standards, guiding delivery teams, resolving complex technical challenges, and serving as a trusted advisor to client stakeholders throughout the solution lifecycle.

Success in this role requires a balance of hands-on architecture leadership, technical depth, stakeholder management, and delivery execution, helping organizations accelerate their Data & AI transformation while ensuring solutions are scalable, governed, performant, and aligned to business objectives.

Key Responsibilities:

Client Partnership & Solution Leadership

• Serve as the primary Databricks architecture lead for client-facing Data & AI initiatives.

• Partner closely with business stakeholders, product owners, enterprise architects, engineering teams, and cloud platform teams to deliver business-aligned solutions.

• Act as a trusted advisor by providing architectural guidance and technical recommendations that balance business requirements, scalability, security, and cost.

• Translate business and functional requirements into scalable data platform architectures and implementation approaches.

• Drive architecture decisions and resolve complex technical challenges throughout the delivery lifecycle.

• Collaborate with client teams to establish engineering standards, solution patterns, and delivery best practices.

• Provide ongoing technical leadership while remaining actively engaged in execution and delivery.

Databricks Architecture & Platform Engineering

• Design and implement enterprise-scale data platforms leveraging the Databricks Lakehouse Platform.

• Lead architecture and implementation of:

  • Delta Lake

  • Unity Catalog

  • Databricks Workflows

  • Databricks SQL

  • MLflow

  • Delta Live Tables (DLT)

  • Lakehouse Monitoring

  • Mosaic AI

• Develop reference architectures, reusable frameworks, and engineering standards to accelerate solution delivery.

• Design scalable batch, streaming, near real-time, and event-driven processing architectures.

• Architect enterprise data ingestion, transformation, storage, consumption, and orchestration layers.

• Design multi-environment deployment strategies supporting development, testing, and production workloads.

• Drive platform performance optimization, scalability, resilience, and operational excellence.

Data Engineering & Data Products

• Architect reusable data pipelines supporting structured, semi-structured, and unstructured data.

• Define scalable data product architectures aligned to business domains and enterprise analytics needs.

• Guide teams on data modeling approaches including Medallion, Dimensional, and Data Vault methodologies.

• Design integration patterns with enterprise applications, APIs, BI platforms, and downstream analytics ecosystems.

• Establish standards for data quality, lineage, observability, metadata management, and operational monitoring.

• Ensure data products are reliable, governed, discoverable, and reusable across the enterprise.

Data Governance, Security & Reliability

• Implement governance frameworks leveraging Unity Catalog and enterprise governance platforms.

• Define standards for data access controls, security, privacy, lineage, and auditability.

• Establish enterprise-wide practices for metadata management, data stewardship, and data ownership.

• Design solutions that meet regulatory, compliance, and enterprise security requirements.

• Partner with security and infrastructure teams to implement scalable and secure data architectures.

• Drive platform observability, monitoring, alerting, and support readiness.

AI / ML & GenAI Enablement

• Design enterprise platforms supporting advanced analytics, machine learning, GenAI, and Agentic AI use cases.

• Enable AI-ready data foundations through strong governance, quality, and data lifecycle management.

• Architect ML lifecycle capabilities leveraging MLflow and Databricks AI services.

• Support implementation of Retrieval-Augmented Generation (RAG) architectures using enterprise data.

• Collaborate with data science teams to operationalize machine learning and AI solutions.

• Define architecture patterns supporting model deployment, monitoring, governance, and scalability.

Cloud Architecture & Integration

• Architect Databricks solutions on Azure, AWS, or GCP environments.

• Design secure integration patterns between Databricks and enterprise systems.

• Guide cloud modernization and migration initiatives from legacy data platforms.

• Define architecture standards for networking, security, identity management, and resilience.

• Collaborate with platform and infrastructure teams to ensure reliability, scalability, and operational efficiency.

Technical Leadership & Delivery

• Provide day-to-day architecture leadership to onsite and offshore engineering teams.

• Review solution designs for scalability, performance, maintainability, and security.

• Mentor data engineers, technical leads, and junior architects.

• Drive engineering best practices, code quality, testing, and deployment standards.

• Support CI/CD, DevOps, Infrastructure-as-Code, and automation initiatives.

• Participate in estimation, technical planning, risk management, and solution reviews.

• Help teams proactively identify and mitigate technical risks and delivery challenges.

Required Experience:

Core Experience

• 10+ years of experience in data engineering, analytics, cloud platforms, or enterprise data architecture.

• 5+ years of hands-on Databricks architecture and implementation experience.

• Proven experience leading architecture and technical delivery for enterprise data platform initiatives.

• Experience working directly with business stakeholders, product teams, and engineering organizations.

• Strong track record designing scalable, secure, and cloud-native data platforms.

• Experience leading distributed onsite/offshore delivery teams.

• Excellent communication, stakeholder management, and client-facing consulting skills.

Databricks Technical Expertise

• Deep expertise with:

  • Databricks Lakehouse Platform

  • Apache Spark

  • PySpark

  • SQL

  • Python

  • Delta Lake

  • Unity Catalog

  • Databricks Workflows

  • Databricks SQL

• Experience building enterprise-scale batch and streaming data solutions.

• Strong understanding of data platform scalability, optimization, performance tuning, and workload management.

• Experience implementing governance, security, and access control models in Databricks.

• Experience designing multi-workspace and enterprise-grade Databricks deployments.

Data Engineering & Architecture

• Strong understanding of:

  • Data Modeling

  • Data Warehousing

  • Data Governance

  • Metadata Management

  • Data Quality

  • Master Data Management

  • Data Product Architecture

• Experience with modern data architecture patterns including Lakehouse, Medallion, and Data Mesh concepts.

• Experience integrating enterprise data ecosystems and analytical platforms.

• Familiarity with dbt, Kafka, APIs, and enterprise data integration patterns.

Cloud & DevOps

• Strong experience with one or more cloud platforms:

  • Microsoft Azure (preferred)

  • Amazon Web Services (AWS)

  • Google Cloud Platform (GCP)

• Experience with CI/CD, DevOps, Infrastructure-as-Code, and automated deployment practices.

• Familiarity with Terraform, Git-based development, monitoring, logging, and operational tooling.

• Understanding of cloud networking, security, authentication, and access management.

AI / ML Enablement

• Experience designing platforms that support machine learning and advanced analytics workloads.

• Familiarity with MLflow, MLOps, model lifecycle management, and AI platform architecture.

• Exposure to GenAI, RAG architectures, vector search, and enterprise AI use cases is preferred.

• Ability to translate business opportunities into practical AI, data, and analytics solutions.

Preferred Experience:

• Experience supporting Fortune 500 enterprise clients.

• Experience in Healthcare, Life Sciences, Pharmaceutical, Distribution, Retail, or highly regulated industries.

• Databricks Certified Data Engineer Professional or equivalent Databricks certification.

• Azure, AWS, or GCP cloud certifications.

• Experience with:

  • Azure Data Factory

  • Azure Synapse Analytics

  • Snowflake

  • Power BI

  • Tableau

  • Kafka

  • Collibra

  • Microsoft Purview

  • Informatica

• Experience supporting AI/ML and GenAI transformation initiatives.

• Experience with enterprise data governance and data product operating models.

• Experience working within a global onsite/offshore delivery model.

Success Measures:

The Lead Databricks Architect will be measured on:

• Successful delivery of enterprise data and AI platforms.

• Platform scalability, reliability, and performance.

• Adoption and business value realization of data products.

• Data quality, governance, and operational maturity.

• Engineering productivity through reusable patterns and standards.

• Successful enablement of AI/ML and GenAI initiatives.

• Reduction of platform costs through optimization and FinOps practices.

• Stakeholder satisfaction and trusted advisor relationships.

• Technical leadership, mentorship, and delivery excellence.

Pay:

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions, including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Fractal, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is: Up to $150,000. In addition, you may be eligible for a discretionary bonus for the current performance period.

Benefits:

As a full-time employee of the company or as an hourly employee working more than 30 hours per week, you will be eligible to participate in the health, dental, vision, life insurance, and disability plans in accordance with the plan documents, which may be amended from time to time. You will be eligible for benefits on the first day of employment with the Company. In addition, you are eligible to participate in the Company 401(k) Plan after 30 days of employment, in accordance with the applicable plan terms. The Company provides for 11 paid holidays and 12 weeks of Parental Leave. We also follow a “free time” PTO policy, allowing you the flexibility to take the time needed for either sick time or vacation.

Fractal provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

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