Data Engineer

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.

We are looking for a Data Engineer (Data Analyst) to join Arctiq dedicated entirely to a high-priority internal data implementation. This is primarily a data engineering role, the successful candidate will be a strong builder who is equally comfortable designing pipelines and data models as they are engaging with business stakeholders to understand requirements.

This is a 3 months contract opportunity (with potential extension).

You will be embedded in our Data and AI practice, working at pace to deliver a unified data platform across five tightly scoped use cases. Databricks is our exclusive platform, and deep hands-on experience with it is essential including Declarative Pipelines (Delta Live Tables) for pipeline development. The right candidate will hit the ground running, drive the work forward independently, and keep stakeholders aligned throughout.

The candidate will work across the following systems and is expected to have direct experience with several of them:

• Net Suite — ERP, financial data, and deal object target for cash-to-cash integration
• Quick Books — accounting and financial records
• Hub Spot — CRM, target state for pipeline and contact data
• Salesforce — CRM, legacy source being transitioned to Hub Spot
• Smartsheet & Resource Manager — resource planning and operational delivery data

• Design, build, and maintain scalable data pipelines in Databricks using Declarative Pipelines (Delta Live Tables), applying medallion architecture principles across bronze, silver, and gold layers
• Build robust data models on Delta Lake that unify data across ERP, CRM, and resource management systems
• Write clean, efficient, and well-documented SQL for transformation, aggregation, and business logic
• Implement data quality frameworks, expectations, and lineage tracking within the Databricks ecosystem
• Configure and manage Databricks workflows, jobs, and cluster resources to support reliable pipeline execution
• Map and load resource and delivery data into ERP deal objects to support the cash-to-cash use case
• Execute the CRM-to-CRM data migration, ensuring complete and accurate translation of historical records

• Develop dashboards and reports that surface actionable insights across all five use cases
• Define and instrument key business metrics in partnership with Finance, Sales, and Operations teams
• Conduct exploratory analysis to surface trends, anomalies, and data quality issues

• Lead discovery and requirements conversations with internal business teams across Finance, Sales, and Operations
• Translate business needs into well-scoped pipeline designs, data models, and reporting deliverables
• Communicate progress, findings, and blockers clearly to both technical and non-technical stakeholders
• Work closely with a Business Analyst to align data outputs with business processes and reporting workflows
• Proactively manage scope and timeline to keep the implementation on track within the 3-month window

• 3+ years of hands-on experience in a data engineering or analytics engineering role
• Deep, production-level experience with Databricks, this is a non-negotiable requirement. Must be comfortable with Delta Lake, Declarative Pipelines (DLT), notebooks, SQL warehouses, jobs/workflows, and cluster configuration
• Hands-on experience with Databricks Declarative Pipelines (Delta Live…