Data Engineer

About WeconnectU

WeconnectU is an intelligent property management software business. Reliable connected data helps us understand customers, improve products, run operations and make sound commercial decisions.

Purpose of the Role

The Data Engineer will design, build and maintain the data foundation that connects WeconnectU's products, CRM, finance systems and operational tools to trusted reporting and analytics. The role combines hands-on data engineering, strong relational and CRM data-model design, a practical understanding of business operations and disciplined project delivery. The successful candidate may use Snowflake or a comparable cloud data platform, depending on WeconnectU's final architecture decision.

You will work across Technology, Product, Finance, Sales, Marketing, Customer Success, Operations and People. Where consultants build parts of the foundation, you will act as WeconnectU's internal delivery owner and ensure the solution is tested, documented, maintainable and properly handed over.

Key Responsibilities

• Build and maintain reliable ETL / ELT pipelines from product databases, finance systems, CRM and other operational sources into the centralised data platform.
• Develop integrations using SQL, APIs, Python and appropriate cloud services.
• Design scalable schemas and data models for shared company, user, CRM, product-usage, finance and operational data, using a cloud data warehouse, lakehouse or equivalent platform.
• Implement incremental loads, testing, logging, retries, alerting and monitoring so failures are visible and recoverable.
• Write readable, secure, version-controlled code and manage platform performance, access, scalability and cost responsibly.

• Map how data moves through end-to-end business processes, from capture and hand-offs to operational action, finance and reporting.
• Define sources of truth, owners, identifiers, business rules, dependencies and reconciliation points for critical data.
• Translate business requirements into data contracts, technical tasks, test cases and measurable acceptance criteria.
• Create automated checks for completeness, validity, uniqueness, consistency, freshness and reconciliation.
• Establish visible exception-handling and issue-ownership processes, and work with teams to fix root causes upstream.
• Design and improve structured CRM and customer-data solutions that support Sales, Marketing, Customer Success, Training, Onboarding and Operations.
• Run focused discovery with departments to understand how data is captured, used and reported, then turn those needs into practical models, pipelines and self-service outputs.
• Maintain clear definitions and lineage for important entities, fields, metrics and calculation logic.

• Prepare trusted datasets and reusable data marts for executive, departmental and product reporting.
• Develop or support dashboards and recurring reports with clear, governed definitions.
• Reduce duplicated and manual reporting while helping teams interpret data and its limitations correctly.

• Turn the data roadmap into a practical backlog with milestones, owners, dependencies, risks and decisions.
• Coordinate internal contributors and external consultants around agreed priorities and business outcomes.
• Define technical scope, interfaces, deliverables and acceptance criteria before build work starts.
• Review designs, code, data models and pipeline outputs; coordinate testing and resolve gaps before sign-off.
• Track progress, costs and risks, and require reusable code, deployment instructions, runbooks, documentation and knowledge transfer.

• Build trusted relationships across departments and act as an approachable first point of contact for data issues.
• Explain technical trade-offs plainly, facilitate focused working sessions and challenge weak assumptions constructively.
• Prioritise requests using business value, urgency, effort and risk.
• Maintain architecture, model, pipeline and operating documentation so solutions are supportable without key-person or consultant dependency.

Key Performance Areas / KPIs

• Reliability and freshness of priority pipelines and datasets against agreed service levels.
• Coverage of priority source systems and improvement in data quality.
• Delivery of milestones within scope, timeline and budget, with risks raised early.
• Quality and acceptance of consultant deliverables,…