Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Big Data Solutions Architect (Professional Services)

Databricks
City of London
1 week ago
Create job alert
Solutions Architect (Professional Services) Spark Expert

Join to apply for the Solutions Architect (Professional Services) Spark Expert role at Databricks.


Location: London


REQ ID: CSQ326R394


Role Overview: In our Professional Services team you will work with clients on short to medium-term engagements to address their big‑data challenges using the Databricks platform. You will provide data engineering, data science, and cloud‑technology projects that require integration with client systems, training, and other technical tasks to help customers extract maximum value from their data. RSAs are billable and must complete projects according to specifications while maintaining excellent customer service. You will report to the regional Manager/Lead.


Impact You Will Have

  • You will work on a variety of impactful customer technical projects, which may include designing and building reference architectures, creating how‑to guides, and productionalizing customer use cases.
  • Work with engagement managers to scope professional‑services work with input from the customer.
  • Guide strategic customers as they implement transformational big‑data projects, third‑party migrations, including end‑to‑end design, build, and deployment of industry‑leading big‑data and AI applications.
  • Consult on architecture and design; bootstrap or implement customer projects that lead to successful understanding, evaluation, and adoption of Databricks.
  • Provide an escalated level of support for customer operational issues.
  • Collaborate with the Databricks technical team, Project Manager, Architect, and Customer team to ensure technical components of each engagement are delivered to meet customer needs.
  • Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution of engagement‑specific product and support issues.

What We Look For

  • 4+ years experience in data engineering, data platforms & analytics; 6+ or 9+ years for senior or expert roles.
  • Comfortable writing code in either Python or Scala.
  • Working knowledge of two or more common cloud ecosystems (AWS, Azure, GCP) with expertise in at least one.
  • Deep experience with distributed computing with Apache Spark™ and knowledge of Spark runtime internals.
  • Familiarity with CI/CD for production deployments.
  • Working knowledge of MLOps.
  • Design and deployment of performant end‑to‑end data architectures.
  • Experience with technical project delivery, managing scope and timelines.
  • Documentation and white‑boarding skills.
  • Experience working with clients and managing conflicts.
  • Skills in deploying and integrating Databricks‑based solutions to complete customer projects.
  • Travel to customers 30% of the time.
  • Databricks Certification.

Benefits

At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all employees. For specific details on the benefits offered in your region, please visit https://www.mybenefitsnow.com/databricks.


About Databricks

Databricks is the data and AI company. More than 10,000 organizations worldwide—including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500—rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics, and AI. Databricks is headquartered in San Francisco, with offices around the globe, and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake, and MLflow.


Seniority Level

Mid‑Senior level


Employment Type

Full‑time


Job Function

Engineering and Information Technology


Industries

Software Development


Compliance

If access to export‑controlled technology or source code is required for performance of job duties, it is within the Employer's discretion whether to apply for a U.S. government license for such positions, and the Employer may decline to proceed with an applicant on this basis alone.


Our Commitment to Diversity and Inclusion

At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical or mental ability, political affiliation, race, religion, sexual orientation, socio‑economic status, veteran status, and other protected characteristics.


#J-18808-Ljbffr

Related Jobs

View all jobs

Big Data Solutions Architect, Spark (Professional Services)

Big Data Solutions Architect (Professional Services)

Big Data Solutions Architect, Professional Services

Big Data Solutions Architect (Professional Services)

Senior Solutions Architect (Big Data) – Outside IR35

Senior Solutions Architect (Big Data) – Outside IR35

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.