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Databricks Architect Jobs in Texas (NOW HIRING)

AI/ML Architect with Databricks, AWS Location: Los Angeles, CA (Hybrid) Hire type: FTE / CTH Role Overview We are seeking an experienced AI/ML Architect with deep hands‑on expertise in Databricks ...

Job Title: AI/ML Architect with Databricks , AWS Remote Role Overview We are seeking an experienced AI/ML Architect with deep hands-on expertise in Databricks on AWS to lead the design and ...

Key Responsibilities - Own architecture standards, data modeling direction, and solution strategies across Snowflake, Databricks, and legacy conversion workstreams. - Chair design reviews across ...

New

Expertise in Databricks architecture, ecosystem, and strategic account planning. * Ability to travel up to 25-50% as required. Pay Range Transparency Databricks is committed to fair and equitable ...

... Lakehouse architecture. *      Convert and optimize Hive SQL, Spark SQL, and PySpark workloads forImple and automation for validation and PySparkjobs from AWS EMR to Databricks ...

Showing results 21-40

Databricks Architect information

What is a Databricks Architect?

A Databricks Architect is an IT professional who designs, implements, and manages data solutions using the Databricks platform, which is built on Apache Spark. They are responsible for creating scalable data pipelines, optimizing data workflows, and ensuring security and compliance within the cloud environment. Databricks Architects often work closely with data engineers, data scientists, and business stakeholders to deliver robust analytics solutions that drive business insights. Their expertise helps organizations leverage big data technologies efficiently and effectively.

What are the key skills and qualifications needed to thrive as a Databricks Architect?

To thrive as a Databricks Architect, you need strong expertise in big data engineering, cloud platforms (such as Azure or AWS), distributed computing, and proficiency in languages like Python or Scala, typically supported by a relevant degree and cloud certifications. Familiarity with Databricks Workspace, Apache Spark, Delta Lake, and CI/CD tools is crucial for designing and implementing scalable data solutions. Excellent problem-solving, communication, and project management skills set top performers apart by enabling effective collaboration and solution delivery. These competencies are essential for architecting reliable, high-performance data platforms that drive business insights and innovation.

What are some common challenges Databricks Architects face when designing large-scale data solutions?

Databricks Architects often encounter challenges such as optimizing cluster performance for cost and efficiency, ensuring data security and compliance across distributed environments, and integrating Databricks with legacy systems or diverse data sources. They must carefully design data pipelines and workflows to handle large volumes of data without bottlenecks, and also collaborate closely with data engineers, data scientists, and IT teams to align on best practices. Staying updated with evolving Databricks features and cloud platform updates is also essential for success in this dynamic role.

What is the difference between Databricks Architect vs Data Engineer?

AspectDatabricks ArchitectData Engineer
Primary FocusDesigning and implementing data solutions on Databricks platformBuilding, maintaining, and optimizing data pipelines and infrastructure
Skills & CertificationsDatabricks certifications, Spark, cloud platforms (AWS, Azure), SQLSQL, ETL tools, cloud platforms, programming (Python, Scala)
Work EnvironmentData platforms, cloud environments, collaboration with data teamsData pipelines, databases, cloud infrastructure, scripting

While both roles work with data and cloud platforms, a Databricks Architect primarily focuses on designing and implementing data solutions using Databricks, whereas a Data Engineer builds and maintains the data pipelines and infrastructure that support these solutions. The Architect often oversees the technical design, while the Engineer handles the day-to-day pipeline development.

What are the most commonly searched types of Databricks Architect jobs in Texas?

The most popular types of Databricks Architect jobs in Texas are:

What job categories do people searching Databricks Architect jobs in Texas look for?

The top searched job categories for Databricks Architect jobs in Texas are:

What cities in Texas are hiring for Databricks Architect jobs?

Cities in Texas with the most Databricks Architect job openings:

Infographic showing various Databricks Architect job openings in Texas as of August 2026, with employment types broken down into 38% Full Time, and 62% Contract. Highlights an 81% In-person, and 19% Remote job distribution.

AI/ML Architect with Databricks , AWS

Vytwo

Prosper, TX • On-site

$150 - $200/hr

Other

Re-posted 21 days ago


Job description

Job Title: AI/ML Architect with Databricks, AWS

Location: Los Angeles, CA (Hybrid)

Hire type: FTE / CTH

Role Overview

We are seeking an experienced AI/ML Architect with deep hands‑on expertise in Databricks on AWS to lead the design and implementation of scalable, high‑performance data and machine learning platforms. The ideal candidate combines architectural thinking with strong engineering execution, demonstrating the ability to build modern lakehouse systems, optimize large‑scale pipelines, and drive analytical and ML capabilities across the organization.

This role requires working with large, multi‑terabyte datasets, advanced analytics, and end‑to‑end ML lifecycle management using Databricks, Python, PySpark, and AWS‑native services.

Must Demonstrate (Critical Competencies)
  • Designing Databricks‑based lakehouse architectures on AWS (Delta Lake + S3 + Unity Catalog).
  • Clear separation of compute vs. serving layers in distributed architectures.
  • Low‑latency API strategy where Spark is insufficient (e.g., leveraging optimized services or caching).
  • Caching strategies to accelerate reads and reduce compute cost.
  • Data partitioning, file size tuning, and optimization strategies for large‑scale pipelines.
  • Experience handling multi‑terabyte structured time‑series workloads.
  • Ability to distill architectural significance from ambiguous business requirements.
  • Strong curiosity, questioning, and requirement‑probing mindset.
  • Player‑coach approach: hands‑on technical depth + ability to guide design.
Key Responsibilities AI/ML & Advanced Analytics
  • Develop, train, and optimize ML models using Python, PySpark, MLflow, and Databricks Machine Learning.
  • Conduct exploratory data analysis (EDA) to identify patterns, trends, and insights in large datasets.
  • Deploy ML models into production using MLflow, Databricks Workflows, or other MLOps pipelines.
  • Build analytics solutions such as forecasting, anomaly detection, segmentation, or recommendation systems.
  • Design ML architectures aligned with Databricks Lakehouse on AWS.
Data Engineering & Lakehouse Architecture
  • Architect and build scalable ETL/ELT pipelines using PySpark, SQL, and Databricks Workflows.
  • Implement Delta Lake best practices, including OPTIMIZE, ZORDER, partitioning, and schema evolution.
  • Design lakehouse layers (Bronze/Silver/Gold) with strong separation of compute and serving layers.
  • Optimize cluster performance and jobs using Spark tuning, caching, and shuffle minimization.
  • Work with multi‑terabyte, time‑series, high‑velocity data in a distributed environment.
  • Ensure robust data availability for downstream ML and analytics workloads.
AWS Cloud Integration
  • Architect end‑to‑end data and ML solutions using AWS services, including:
  • S3 for storage
  • IAM for identity & access
  • Glue Catalog for metadata management
  • Networking for secure, high‑throughput data movement
  • Integrate Databricks with AWS‑native compute, API layers, and low‑latency endpoints.
Business Collaboration & Leadership
  • Translate business problems into scalable analytical or ML architectures.
  • Communicate complex statistical and architectural concepts to non‑technical stakeholders.
  • Collaborate with product, engineering, and business leaders to drive data‑informed initiatives.
  • Provide design leadership while remaining hands‑on in execution.
Skills & Qualifications Required
  • Bachelor’s or Master’s in Computer Science, Data Science, Engineering, Statistics, or related field.
  • 10+ years of experience in data engineering, ML engineering, or AI/ML architecture roles.
  • Deep expertise in Databricks on AWS, including:
  • PySpark / Spark SQL
  • Databricks Notebooks
  • Delta Lake
  • Unity Catalog
  • MLflow
  • Databricks Jobs & Workflows
  • Strong programming ability in Python (pandas, numpy, scikit‑learn).
  • Demonstrated experience with large‑scale, multi‑terabyte data processing.
  • Strong understanding of ML algorithms, distributed systems, and data optimization.
Preferred
  • Experience with MLOps and production deployment pipelines.
  • Strong grasp of AWS‑native data and compute services.
  • Understanding of CI/CD using GitHub Actions, GitLab CI, or similar.
  • Familiarity with deep learning frameworks (TensorFlow, PyTorch).
Key Competencies
  • Strong analytical and problem‑solving skills.
  • Ability to work in fast‑paced, highly collaborative environments.
  • Excellent communication and presentation abilities.
  • Self‑driven with exceptional attention to architectural detail.

Flexible work from home options available.

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