UTIS delivers engineer-led consulting for data platforms, AI and machine learning, with technical staffing to strengthen your team.
Logos open official product sites in a new tab. Select a technology name to explore UTIS work and delivery scope.
01 / Selected work
Three engagements, with their delivery and measurement scope.
Semiconductor manufacturing
Databricks
About 57% lower average daily compute cost across 11 pipelines, comparing the initial post-migration days with a five-month baseline.
Project-reported · initial post-migration observation
Title insurance
Power BI
Reporting models, published dashboards and data validation developed with a business intelligence team.
Client-confirmed delivery · reporting and source validation
Media
Snowflake
Semantic models, saved questions and charts in a Snowflake application delivered for client review.
Client-confirmed delivery · application delivered for review
Each case explains the contribution and its scope. References require client permission.
02 / Client feedback
Candidate quality
“Timely, need-based, and cost-effective solutions. … High quality candidates.”
Snowflake expertise
“When I asked them for a Snowflake resource, they responded quickly and delivered a strong consultant who got up to speed fast and contributed right away.”
Technical ownership
“They approach our problems as their own and consistently deliver thoughtful, practical solutions.”
Embedded engineering
“Depth of knowledge is impressive. Big fan of engineering people who are onsite and collaborate with users.”
Power BI delivery
“Together we've also built out Power BI analytics and dashboards, CRM data integrations, and client-specific metrics that we rely on to manage the business.”
Repeat engagements
“We have since called on UTIS regularly to step in on our most important data management challenges. They are expert, adaptable, and efficient.”
Team collaboration
“UTIS has been a dependable extension of our team, and I recommend them without reservation.”
Data engineering
“I would recommend UTIS to any organization looking for a dependable data and engineering partner.”
Client perspective
“They approach the work from our point of view, which is why our collaboration endures.”
Staffing delivery
“UTIS hit it out of the park…”
Client feedback excerpts. Personal, project and client identities are withheld. Ellipses mark omitted wording; context remains specific to each engagement.
Read all client feedback03 / Industry experience
Different systems and business rules. Five distinct paths into our experience.
Machine-data pipelines, engineering analysis and reporting in manufacturing environments.
Property reporting, data quality and controlled history updates with SQL Server and SSIS.
Power BI reporting and SQL source checks for a business intelligence team.
Snowflake analytics applications, SQL Server data operations and reporting foundations.
Power Pages portal architecture, permissions and controlled application releases.
Read the relevant scope and client context on each industry page.
04 / Consulting
Build, modernize and support your data platforms, AI applications, cloud infrastructure and business systems—from architecture through delivery and handover.
Build and validate pipelines with Python and PySpark, improve data quality and modernize your data platform.
Build Azure infrastructure, automate deployments with Terraform and Azure DevOps, and monitor running workloads.
Develop AI applications, validate model outputs and prepare machine-learning workflows for delivery.
Build portals, approval workflows and connected business applications with clear permissions and deployment controls.
Modernize pipelines and reporting models with PySpark, incremental Delta processing and repeatable deployment.
Improve query performance, data-loading reliability, recovery and monitoring across your database estate.
Build reporting models and dashboards with agreed business measures and source-data checks.
Turn business questions into semantic models and usable analytics applications with Cortex Analyst and Streamlit.
Professional qualifications
Professional qualifications in data, AI, cloud engineering and analytics.
Databricks GenAIDatabricks · Valid through February 28, 2028
Issued February 28, 2026
About this qualification (issuer program information; opens a new tab)
Azure AI Apps & AgentsMicrosoft · Valid through September 28, 2027
Issued September 28, 2026
About this qualification (issuer program information; opens a new tab)
DevOps EngineerMicrosoft · Valid through January 10, 2027
Issued January 10, 2026
About this qualification (issuer program information; opens a new tab)Power BI Data AnalystMicrosoft · Valid through October 31, 2027
Issued October 31, 2026
About this qualification (issuer program information; opens a new tab)
Google Cloud ArchitectGoogle Cloud · Valid through September 13, 2028
Issued September 13, 2026
About this qualification (issuer program information; opens a new tab)05 / Staffing
Hire data, AI, cloud and software engineers for your team. Recruiting is led by a software architect with recruiting experience since 2011 and practical experience delivering production systems.
Assess production judgment, ownership and the ability to turn unclear requirements into a clear problem statement.
Specialists examine relevant skills, domain understanding and the practical demands of your role.
Understand the candidate’s strengths, gaps and fit before your team invests time in interviews.
Add engineers for your technology and domain, with working-hour overlap and budget agreed around your team’s needs.
Explore paid internships and a possible path from a UTIS assignment to direct client employment.
Understand the sponsorship and onboarding process, with each case reviewed before commitments.
Many of our consulting engagements began with staffing. As your needs develop, we can agree a broader project or support scope.
06 / Working together
A defined project, an embedded engineer or ongoing platform support.
Give a defined problem a clear owner. We agree the scope, milestones, acceptance criteria and handover before implementation.
Review the current systems and decide whether a focused assessment is useful.
Add an engineer assessed by a UTIS practitioner in the relevant stack. Your team sets priorities; UTIS stays involved in technical fit, feedback and continuity.
Agree the role, responsibilities, work location, availability and interview process.
Define ongoing responsibility for selected data pipelines, database operations or cloud services. Coverage, escalation and service commitments are agreed for your environment.
Identify the systems, support boundaries and transition requirements.
Agree the location, working-hour overlap and commercial approach around your team’s needs.
What you can expect
Set responsibilities, milestones and acceptance criteria before work begins.
Review completed work, validation and next steps together.
Retain the decisions, operating steps and handover responsibilities.
Discuss your MSA, screening, insurance and access requirements early.
Who is behind the work?
Founder & CEO · Data Architect
Arun Albert founded UTIS in 2011. His hands-on background in SQL Server, SSIS, data architecture and production troubleshooting shapes how the firm scopes work and assesses engineers.
07 / Before we start
Fit, delivery, technical evidence and what to expect from a first conversation.
UTIS is an engineer-led IT consulting and technical staffing firm founded in 2011 and based in Irvine, CA. We help enterprise teams build, modernize and support data platforms, analytics, cloud systems, applied AI and business applications. Our staffing approach combines leadership by a software architect with specialist technical and domain interviews and a written assessment of strengths, gaps and fit. We work through technical staffing, embedded engineers, scoped projects and ongoing support, with experience in semiconductor manufacturing, property data, title insurance, media and energy.
Our work spans semiconductor manufacturing, property data, title insurance, media analytics and energy. The project pages explain the experience relevant to each area. We also assess work in other industries when the technical requirements fit our expertise.
Yes. Our documented Azure Databricks work includes machine-log pipeline modernization, incremental Delta processing, selected SAP HANA reporting models and engineering applications. The practice page links to delivery evidence and explains our approach to assessment, validation and handover.
Choose a preferred weekday time for a 30-minute conversation and share a little context. Times are shown in Pacific Time and require at least two days’ notice. UTIS will confirm the meeting details by email. Your selected time is a preference until we confirm it, and requesting a meeting does not commit you to an engagement.
Yes. We can discuss your MSA or ours, time-and-materials or milestone-based statements of work, and the screening, insurance, access and onboarding requirements for the engagement. Responsibilities and commercial terms are agreed before work begins.
Yes. Start with our anonymized case studies and client feedback. We can discuss the relevant scope and, with client permission and appropriate confidentiality arrangements, reference conversations. Public pages omit client identities and sensitive operational details.
08 / Start a conversation
Choose a preferred time for a 30-minute conversation about a technical role, platform, reporting problem or delivery need. We’ll confirm the meeting details by email.
300 Spectrum Center Drive, Suite 400, Irvine, CA 92618
30-minute conversation
Book a meetingChoose a weekday time in Pacific Time. Your selection is a preference until UTIS confirms the meeting by email.
