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Senior Data Technology Consultant, Databricks Data & AI Practice
- Pay
- Pay not listed
- Work setting
Remote — work setting source
Work setup in the employer’s posting: TELECOMMUTE
From the employer’s posting- Employment
- Not stated
What you’ll work on
Full postingDesign Lakehouse architectures and AI solutions on Databricks
Own pre-sales solution design, technical scoping, and effort estimation
Run client workshops to define target-state architecture and adoption path
Craft proposals, pricing, and engagement models with Databricks account teams
Guide delivery teams from advisory through production
From the employer’s posting
Design Lakehouse architectures and AI solutions on Databricks
Own opportunity solution design, technical scoping, and effort estimation for Databricks engagements
Run client workshops to capture business objectives, assess current-state data platforms, and define the target-state architecture and Databricks adoption path
Craft proposals, pricing and engagement models, and value propositions. Work with Databricks account teams to win and expand engagements
Guide teams from advisory through production
Posting record
Report an errorActively hiringFirst seen by us 1 day ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 5, 20261 day ago
- Recorded sightings
- 1
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
EPAM Systems is hiring
662 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- SQL
- AWS
- Azure
- Databricks
- dbt
- Delta Lake
- Fivetran
- GCP
- MLflow
- Power BI
- Tableau
- Terraform
Where the posting names them
We are looking for seasoned Senior Data Technology Consultants to join our Databricks Data & AI Consulting Practice. You will own end-to-end Databricks initiatives. In pre-sales, you will design the Lakehouse and AI solution. After the win, you will lead strategic client engagements from advisory through production. This is a hands-on leadership role for technologists who combine deep Databricks expertise with commercial ownership and client impact. Req.#1128476334 Responsibilities Architect Databricks Solutions: Design Lakehouse architectures and AI solutions on Databricks. This covers Delta Lake, Unity Catalog governance, Lakeflow/Delta Live Tables pipelines, Databricks SQL, and Mosaic AI (ML, GenAI, agents, Model Serving). Where needed, integrate with cloud services and partner technologies Lead the Presales Lifecycle: Own opportunity solution design, technical scoping, and effort estimation for Databricks engagements, including migrations to Databricks, platform builds, and AI/ML use cases Facilitate Discovery: Run client workshops to capture business objectives, assess current-state data platforms, and define the target-state architecture and Databricks adoption path Drive Commercial Success: Craft proposals, pricing and engagement models, and value propositions. Work with Databricks account teams to win and expand engagements Lead Delivery: Guide teams from advisory through production, applying Databricks best practices for performance, cost optimization (FinOps), security, CI/CD, and MLOps/LLMOps Requirements Databricks Expertise: Deep, hands-on experience across the Databricks platform. This includes Spark, Delta Lake, Unity Catalog, Workflows, Lakeflow/DLT, Databricks SQL, and Mosaic AI/MLflow. You should have a track record of leading complex, enterprise-scale Databricks implementations, such as platform builds, data migrations, and ML/GenAI production deployments Cloud & Ecosystem: Strong working knowledge of at least one major cloud (AWS, Azure, or GCP) and of the partner technologies commonly used with Databricks, such as dbt, Fivetran, Power BI/Tableau, and Terraform Consulting & Presales Background: Substantial experience in presales, solution architecture, or consulting centered on Databricks and data/AI use cases Communication & Storytelling: Outstanding ability to translate complex Lakehouse and AI concepts into clear, client-friendly narratives for senior stakeholders Commercial & Proposal Acumen: Proven ability to produce detailed proposals, RFP responses, and pricing and engagement models for Databricks-based services Strategic Advisory Skills: Strong grasp of consulting principles, with the ability to define future-state recommendations, roadmaps, and project scopes in fast-paced settings Must have: Databricks certifications (e.g., Data Engineer Professional, Machine Learning Professional, or Generative AI Engineer) and experience with Databricks partner programs
Your next step
Complete your application on careers.epam.com. The employer’s form will show what is required.
Already applied? Track this application
Has EPAM Systems posted this job before?
Yes. We’ve followed 2 other Senior Data Technology Consultant, Databricks Data & AI Practice jobs there since November 2026. All 2 are still up.
About EPAM Systems
- People
- About 3,012 in 2024.Benefit plan filings (Form 5500)
- Company
- Listed as EPAM.
- H-1B visas
- 21 approved for new hires in fiscal 2025.US Citizenship and Immigration Services
Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
We are looking for seasoned Senior Data Technology Consultants to join our Databricks Data & AI Consulting Practice. You will own end-to-end Databricks initiatives. In pre-sales, you will design the Lakehouse and AI solution. After the win, you will lead strategic client engagements from advisory through production. This is a hands-on leadership role for technologists who combine deep Databricks expertise with commercial ownership and client impact. Req.#1128476334 Responsibilities Architect Databricks Solutions: Design Lakehouse architectures and AI solutions on Databricks. This covers Delta Lake, Unity Catalog governance, Lakeflow/Delta Live Tables pipelines, Databricks SQL, and Mosaic AI (ML, GenAI, agents, Model Serving). Where needed, integrate with cloud services and partner technologies Lead the Presales Lifecycle: Own opportunity solution design, technical scoping, and effort estimation for Databricks engagements, including migrations to Databricks, platform builds, and AI/ML use cases Facilitate Discovery: Run client workshops to capture business objectives, assess current-state data platforms, and define the target-state architecture and Databricks adoption path Drive Commercial Success: Craft proposals, pricing and engagement models, and value propositions. Work with Databricks account teams to win and expand engagements Lead Delivery: Guide teams from advisory through production, applying Databricks best practices for performance, cost optimization (FinOps), security, CI/CD, and MLOps/LLMOps Requirements Databricks Expertise: Deep, hands-on experience across the Databricks platform. This includes Spark, Delta Lake, Unity Catalog, Workflows, Lakeflow/DLT, Databricks SQL, and Mosaic AI/MLflow. You should have a track record of leading complex, enterprise-scale Databricks implementations, such as platform builds, data migrations, and ML/GenAI production deployments Cloud & Ecosystem: Strong working knowledge of at least one major cloud (AWS, Azure, or GCP) and of the partner technologies commonly used with Databricks, such as dbt, Fivetran, Power BI/Tableau, and Terraform Consulting & Presales Background: Substantial experience in presales, solution architecture, or consulting centered on Databricks and data/AI use cases Communication & Storytelling: Outstanding ability to translate complex Lakehouse and AI concepts into clear, client-friendly narratives for senior stakeholders Commercial & Proposal Acumen: Proven ability to produce detailed proposals, RFP responses, and pricing and engagement models for Databricks-based services Strategic Advisory Skills: Strong grasp of consulting principles, with the ability to define future-state recommendations, roadmaps, and project scopes in fast-paced settings Must have: Databricks certifications (e.g., Data Engineer Professional, Machine Learning Professional, or Generative AI Engineer) and experience with Databricks partner programs
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