Company Overview:
Arctiq is a global, intelligence-driven technology services company delivering professional and managed services across Hybrid Cloud Infrastructure, Networking & Connected Experiences, Cybersecurity, Data & AI, Autonomous Operations & Intelligence, and Enterprise Service Management. We help organizations operate, secure, and modernize complex environments by unifying infrastructure, networking, data, security, automation, and observability under a single, integrated operating model. Our work focuses on helping customers reduce operational friction, improve resilience, and make better, faster decisions as their environments evolve. Arctiq builds on decades of industry expertise and a customer-centric ethos to deliver exceptional value to clients across diverse industries.
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