Data Engineer - PySpark, Databricks

Tenth Revolution Group; City Of London; Hybrid; GBP 50,000 - 75,000; Full time

Job summary

A rapidly growing B2B SaaS company is seeking a Deployed Engineer to join their expanding London team. The role involves adapting and deploying a sophisticated data platform and designing AI workflows. Ideal candidates should have strong skills in PySpark, Python, and SQL. The position offers a salary between £50,000 and £75,000, along with share options and a hybrid working model, emphasizing a vibrant culture with team collaboration.

Benefits

Salary: £50,000 - £75,000 + share options

Hybrid working: 2-3 days per week in a vibrant office

Social team events and activities

Opportunity to work with tech and business leaders

Qualifications

• Strong skills in PySpark, Python, and SQL.
• Ability to translate ambiguous requirements into clean, maintainable pipelines.
• Quick learner with a passion for new technologies.

Responsibilities

• Adapt and deploy a powerful data platform to solve complex business problems.
• Design scalable generative AI workflows using modern platforms.
• Execute advanced data integration using PySpark and distributed technologies.
• Collaborate directly with clients to understand priorities.

Skills

PySpark

Python

SQL

Customer-facing collaboration

Tools

Palantir AIP

Typescript

Airflow

DBT

Databricks

SAP

Oracle

Job description

A rapidly growing company in the B2B Software as a Service (SaaS) space are looking for a Deployed Engineer to join their expanding team in London (hybrid working - 2-3 days a week in their modern office space).

Their product is a platform that acts as a "digital twin" of a their customers businesses - integrating internal and external data from a variety of sources to act as a "single source of truth", powering actionable insights at scale. When combined with AI algorithms, the platform drives strategic decision-making, and enables planning and effective execution, allowing businesses to achieve their targeted state. They are a true pioneer in their field!

They believe the future of B2B SaaS is about delivering tailored, dynamic solutions for their clients, rather than implementing static tools. This is where you come in - you'll be working within a team who believe value is created not just in the codebase, but in the implementation layer - making this role ideal for someone who thrives in dynamic, customer-facing environments.

The role:

• Adapt and deploy a powerful data platform to solve complex business problems
• Design scalable generative AI workflows using modern platforms like Palantir AIP
• Execute advanced data integration using PySpark and distributed technologies
• Collaborate directly with clients to understand priorities and deliver outcomes

What We're Looking For:

• Strong skills in PySpark, Python, and SQL
• Ability to translate ambiguous requirements into clean, maintainable pipelines
• Quick learner with a passion for new technologies
• Experience in startups or top-tier consultancies is a plus

Nice to Have:

• Familiarity with dashboarding tools, Typescript, and API development
• Exposure to Airflow, DBT, Databricks
• Experience with ERP (e.g. SAP, Oracle) and CRM systems

What's On Offer:

• Salary: 50,000- 75,000 + share options
• Hybrid working: 2-3 days per week in a vibrant Soho office
• A highly social culture with regular team events and activities
• Work alongside seasoned tech and business leaders
• Be part of a mission-driven company with a strong social impact ethos

If you're excited by the idea of working at the intersection of AI, data, and enterprise transformation - and want to be part of a fast-scaling, values-led team - we'd love to hear from you.

Please Note: This is a role for UK residents only. This role does not offer Sponsorship. You must have the right to work in the UK with no restrictions. Some of our roles may be subject to successful background checks including a DBS and Credit Check.

We are an equal opportunities employer and welcome applications from all qualified candidates.