Position Overview:
The Solution Engineer, Data & AI is a hands-on builder who designs, delivers, and operates production-grade data and AI solutions. You will join a team of engineers who bring real software engineering discipline to data work - version control, automated testing, CI/CD, infrastructure as code, and observability are how we work, not afterthoughts. We also expect our engineers to embrace agentic engineering, using tools like Claude Code to dramatically accelerate how software gets built without compromising quality.
Depending on business need and your strengths, you will be aligned to one of two paths:
Path 1 - Client Delivery: Support delivery of fixed-price, outcome-based data and AI projects for clients across a wide range of industries and problem domains, on Azure, AWS, and GCP. In a fixed-price model, engineering quality and delivering within agreed scope directly affect client outcomes and project margin.
Path 2 - Internal Data & Analytics: Bring order to our internal data on Azure Databricks - integrating, cleaning, and modelling data from our business systems so the organization has trusted analytics and reporting and helping automate core business processes such as quote-to-cash.
Both paths share the same technical foundation, and engineers may move between paths over time as priorities and interests evolve.
Responsibilities:
Data Pipeline Engineering
- Design, build, and maintain reliable, scalable data pipelines for batch and streaming workloads
- Apply proven lakehouse architecture patterns, with clear data contracts and well-managed schema evolution
- Build data quality checks, testing, and monitoring into every pipeline from day one
Data Modelling & Analysis
- Design analytical data models that reflect business requirements and perform at scale
- Profile, explore, and analyze data to validate assumptions, surface quality issues, and share meaningful insights with stakeholders
- Apply sound data governance practices so data assets are secure, traceable, and discoverable
AI & Machine Learning Engineering
- Build, train, and evaluate machine learning and generative AI solutions - from classical ML to LLM-based and agentic applications
- Establish rigorous evaluation approaches tied to clear, business-aligned success criteria
- Take models from experimentation to production, ensuring they are reliable, observable, and maintainable
MLOps & DevOps
- Build and maintain CI/CD pipelines for code, data, and models
- Manage cloud and platform resources through infrastructure as code
- Implement full model lifecycle management - versioning, deployment, monitoring, drift detection, and retraining
- Champion engineering best practices across the team: code reviews, branching strategies, automated testing, and documentation
Agentic Engineering
- Use agentic coding tools (e.g., Claude Code) as a core part of your daily workflow to accelerate design, development, testing, documentation, and refactoring
- Provide AI agents with effective context through clear specifications, project instructions, and reusable workflows
- Apply rigorous review, testing, and validation to AI-generated code - you own the quality of what ships, regardless of who or what wrote it
- Share effective agentic workflows with the team and help codify them into repeatable practices and accelerators
Path 1 - Client Delivery
- Deliver engineering workstreams on fixed-price client engagements, from technical discovery through outcome acceptance
- Work across client environments on Azure, AWS, and GCP, adapting patterns to each client's cloud, security model, and constraints
- Partner with Architects and your Engineering Manager to deliver solutions within agreed scope, timeline, and commercial constraints
- Participate in client workshops, demos, and technical working sessions, building credibility with client technical teams
- Identify scope creep, technical risks, and blockers early - and raise them promptly and constructively
- Leave clients better off: document solutions clearly, transfer knowledge, and help build client-side capability
- Contribute to reusable accelerators, templates, and patterns that make future engagements more predictable
Path 2 - Internal Data & Analytics
- Integrate data from our core business systems (e.g., CRM, PSA, finance/ERP, HR) into our Azure Databricks environment
- Clean, reconcile, and standardize messy internal data - resolving duplicates, aligning definitions across systems, and establishing a single source of truth for key entities such as customers, projects, contracts, and revenue
- Coordinate with internal teams to identify, prioritize, and resolve data quality issues at the source, and put processes in place to prevent them from recurring
- Build trusted, well-documented data models that power internal analytics, KPIs, and executive and operational reporting
- Partner with Finance, Sales, Operations, and leadership to understand reporting needs and translate them into reliable data products
- Help design and automate quote-to-cash workflows, connecting data and handoffs from opportunity through quote, contract, delivery, invoicing, and collections
Qualifications:
- 4+ years in software and platform engineering, including meaningful time on a collaborative software development team
- Hands-on experience building production data and ML solutions in the cloud
- Strong, demonstrable experience with the Databricks platform in production environments
- Practical experience using agentic coding tools to accelerate software development - with examples of how it has changed the way you work
Technical Skills
Software Engineering & DevOps
- Strong proficiency in Python and SQL
- Solid software engineering fundamentals: Git workflows, code review, unit and integration testing, modular design
- Hands-on experience with CI/CD tooling and infrastructure as code
- Comfortable with containerization, environment management, and automated deployment
Cloud Platforms
- Working knowledge of all three major clouds - Azure, AWS, and GCP - with deep hands-on experience in at least one
- Understanding of how data platforms are deployed and integrated on each cloud, including storage, identity, networking, and security
- Familiarity with each cloud's native data and AI services and when to use them alongside Databricks
Databricks
- Deep, hands-on experience across the Databricks platform, including data engineering, governance, and ML/AI capabilities
- Experience deploying and managing Databricks solutions using software engineering and DevOps best practices
Data Engineering & Modelling
- Proven ability to design and build scalable batch and streaming pipelines
- Strong data modelling skills across dimensional and modern lakehouse patterns
- Experience with data quality frameworks, testing, and observability
AI / ML & MLOps
- Practical experience building, evaluating, and deploying ML models to production
- Experience with generative AI patterns such as retrieval-augmented generation, agents, and LLM evaluation
- Strong MLOps practices across the full model lifecycle
Agentic Engineering
- Hands-on proficiency with agentic development tools such as Claude Code
- Ability to decompose work, write clear specifications, and manage context so agents produce high-quality results
- Sound judgment on where AI-assisted development adds leverage and where it introduces risk, with a disciplined approach to reviewing and testing its output
