Databricks Practice Architect
TEKsystems
Job Overview
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Job Description
About TEKsystems Global Services
Think of TEKsystems Global Services (TGS) as the growth solution for enterprises today. We unleash growth through technology, strategy, design, execution and operations with a customer-first mindset for bold business leaders. We deliver cloud, data and customer experience solutions. Our partnerships with leading cloud, design and business intelligence platforms fuel our expertise.
We value deep relationships, dedication to serving others and inclusion. We drive positive outcomes for our people and our business, and we stay true to our commitments and act in harmony with our words. We exist to create significant opportunity for people to achieve fulfillment through career success.
Ready to join us?
The Opportunity: Databricks Practice Architect
Join our Data Modernization and Gen AI practice and take the lead across designing, building, and maintaining data pipelines and analytics solutions on the Databricks cloud platform. You will collaborate with data analysts and customer stakeholders to ensure the availability, scalability, and reliability of data.
The Individual
- Consultative and personable data engineering leader who will provide advice and leading expertise on Databricks.
- Possess a deep understanding of cloud data services to become a key contributor to various large-scale data projects.
- Works on specific projects critical to our needs with opportunities to switch teams and projects as our fast-paced business grows and evolves.
- Is versatile, displays leadership qualities and is enthusiastic to take on new challenges across the data stack.
Qualifications
Minimum qualifications:
- Bachelor’s or higher degree in Computer Science, Information Technology, or a related field.
- 10+ years industry experience building and supporting large-scale cloud systems.
- Ability to speak and write in English fluently.
- Excellent verbal and written communication skills.
Preferred qualifications:
- Professional Certification in one or more areas: Cloud Architecture, Data Engineering, Data Analyst, ML Engineer.
Job Requirements
Experience:
- Experience with Databricks, Spark and working knowledge of DBT for data processing and transformation.
- Working understanding of Data Warehousing concepts of Data Vault, Dimensional modelling, OLAP design.
- Hands-on experience building data pipelines using technology such as Azure Data Factory, AWS Glue, GCP Dataflow and Apache Spark (preferably in Databricks).
- Extensive experience with CI/CD platforms such as GitLab CI, GitHub Actions, AWS CodeBuild, Azure Pipelines, GCP Cloud Build, and Jenkins.
- Skilled in scripting languages including Python, SQL, R, Scala, Bash, and PowerShell.
- Familiarity with log and monitoring solutions.
- Experience in utilizing development containers, unit testing/code quality review - standard best practices.
- Proficiency in Python and SQL for data transformation and querying.
- Strong expertise in version control systems.
- Familiarity with containerization and orchestration technologies.
- Strong problem-solving, troubleshooting, communication and collaboration skills.
- Familiarity with Generative AI Large Language Models (LLMs), Retrieval Augmented Generation (RAG), and Agentic AI techniques.
- Databricks certified candidates will be preferred.
Responsibilities:
- Design & Architecture: Design and implement solutions for the Databricks platform and APIs. Work with data engineering and data science teams, customers, and other customer-facing teams.
- Technical leadership: Provide technical guidance to customers on big data projects, including architectural design, data engineering, and model deployment.
- Customer collaboration: Work with customers to understand their requirements, and help them define solutions and a roadmap to meet their goals.
- Solution design: Create and review architecture and solution design artifacts.
- Technical expertise: Demonstrate expertise in Databricks Lakehouse architecture, delta lake medallion architecture, data pipelines, CI/CD pipelines etc.
- Communication: Communicate complex technical concepts to non-technical stakeholders, such as business leaders and C-level executives.
- Documentation: Develop and maintain documentation for the Databricks platform, such as deployment guides, operational procedures, and architecture diagrams.
- Risk management: Identify, communicate, and mitigate risks, assumptions, issues, and decisions.
We reserve the right to pay above or below the posted wage based on factors unrelated to sex, race, or any other protected classification.
Additional earnings may be available through incentive programs like annual bonuses, profit sharing, etc.
Please click on the following link to learn more about our full-time internal employment benefits: https://www.teksystems.com/en/careers/benefits.
The expected posting close date is February 26, 2026.
Key skills/competency
- Databricks
- Data Engineering
- Cloud Architecture
- Spark
- Data Pipelines
- Generative AI
- SQL
- Python
- CI/CD
- Data Warehousing
How to Get Hired at TEKsystems
- Research TEKsystems' culture: Study their mission, values, recent news, and employee testimonials on LinkedIn and Glassdoor. Understand their focus on growth solutions and customer-first mindset.
- Tailor your resume for Databricks Practice Architect: Customize your resume to highlight extensive experience in Databricks, Spark, cloud data systems, and leadership in data modernization. Use keywords from the job description like 'Generative AI', 'CI/CD pipelines', and 'Lakehouse architecture'.
- Showcase relevant project experience: Prepare to discuss specific large-scale cloud projects, data pipeline implementations, and architectural designs on platforms like Databricks, Azure, AWS, or GCP during interviews. Emphasize your consultative approach and problem-solving skills.
- Highlight communication and leadership: TEKsystems values strong communication and technical leadership. Be ready to provide examples of how you've communicated complex technical concepts to non-technical stakeholders and led data engineering initiatives.
- Prepare for technical and behavioral interviews: Brush up on Databricks Lakehouse architecture, data warehousing concepts, scripting (Python, SQL), and CI/CD. Also, practice answering behavioral questions that demonstrate collaboration, problem-solving, and customer focus.
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