Data Engineer

Liquid Thought; Somerset West; On-site; ZAR 550,000 - 900,000; Full time

Job summary

Liquid Thought, a data-driven property management software business, is seeking a Data Engineer to design, build and maintain the data foundation that connects products, CRM, finance systems and operations to trusted reporting and analytics.

You may use Snowflake or a comparable cloud data platform, based on the final architecture decision; you will work across Technology, Product, Finance, Sales, Marketing, Customer Success, Operations and People to ensure tested, documented, maintainable

Qualifications

• 4 to 6 years of practical experience in data engineering or related roles.
• Hands-on production experience with Snowflake or comparable cloud data platform.
• Strong SQL skills plus automation, testing and production support.
• Experience designing and supporting ETL/ELT pipelines and API integrations.
• Strong relational and dimensional modelling including CRM data models.

Responsibilities

• Build and maintain a central data platform connecting product, CRM, finance, and ops.
• Develop integrations using SQL, APIs, Python and cloud services.
• Design scalable data schemas for shared company and CRM data.
• Implement incremental loads, testing, logging, retries, and monitoring.
• Map data flows across end-to-end business processes.
• Define data contracts, owners, business rules, and reconciliation points.
• Prepare trusted datasets and dashboards for reporting.
• Coordinate internal and external delivery, track progress and risk.

Skills

SQL

ETL/ELT pipelines

Snowflake

CRM data models

Git

BI tools

Data governance

POPIA

Education

Relevant degree or diploma

Tools

dbt

AWS data services

Job description

Data Engineer

Location: Somerset West, Western Cape (in-office)

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

Purpose of the Role

You will design, build and maintain the data foundation that connects the company'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. You may use Snowflake or a comparable cloud data platform, depending on the 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 act as the internal delivery owner and make sure the solution is tested, documented, maintainable and properly handed over.

Key Responsibilities

Data engineering and a centralised data platform

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

Operational data flows and quality

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

Analytics and reporting enablement

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

Project and consultant delivery

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

Cross-functional partnership

• Build trusted relationships across departments and be the approachable first point of contact for data issues.
• Explain technical trade-offs plainly, run working sessions and challenge weak assumptions constructively.
• Prioritise requests by business value, urgency, effort and risk.
• Keep architecture, model, pipeline and operating documentation current so solutions do not depend on one person or consultant.

Requirements

• 4 to 6 years of practical experience in data engineering, analytics engineering, BI engineering or a closely related role.
• Hands-on production experience with Snowflake or a comparable cloud data warehouse or lakehouse: data loading, schema design, SQL transformations, role-based access, monitoring and performance or cost optimisation. Snowflake is advantageous, but the ability to design the right solution matters more than prior use of one named platform.
• Very strong SQL, with practical experience in automation, integration, testing and production support.
• Experience designing, building and supporting ETL / ELT pipelines, API integrations, scheduled workflows and incremental loads.
• Strong relational and dimensional modelling, including CRM data models, shared customer or company records, cross-system identity matching and history tracking.
• Experience mapping operational processes and delivering data solutions across multiple departments.
• Strong project coordination: requirements, plans, dependencies, risks, testing and delivery follow-through. Experience managing consultants is strongly preferred.
• Working knowledge of Git, BI tools, data quality, governance, security, privacy and POPIA principles.
• Clear written and verbal communication, and a relevant degree, diploma or equivalent practical experience.

Advantageous

• AWS data services, dbt or other orchestration tools.
• Multi-product SaaS, property management, finance operations or CRM data.