Data Engineer

Safewill

Data Engineer position focusing on scalable data solutions for estate planning startup Safewill. Responsibilities include designing pipelines and collaborating across functions to enhance data delivery standards.

Posted 8/1/2026full-timeSydney • 🇦🇺 AustraliaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and maintaining scalable data pipelines and models, with a strong focus on data quality, performance optimization, and integration of AI tools for analytics. Proven ability to collaborate effectively with teams to translate complex requirements into actionable technical solutions.

Highest-signal resume keywords

Data Pipeline DevelopmentAdvanced SQL SkillsPython ProgrammingCloud Data Warehouse ExperienceCI/CD Practices

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data PipelineData ModelSQLPythonData TransformationData Workload OptimizationData QualityTestingMonitoringAI Tools

Soft Skills

CollaborationProblem-Solving

Tools & Technologies

Cloud Data WarehouseAI Tools

Industry Keywords

Data InfrastructureEnterprise IntegrationsOperational Workflows

Tech Stack

Tools & technologies

CloudPythonSQL

About the role

Key responsibilities & impact

Design, build and maintain scalable data pipelines and models for product features.
Activate data through embedded products, enterprise integrations, and operational workflows.
Develop and govern integrations connecting AI agents with Safewill's data and tools.
Optimize data workloads for performance, reliability, and cost management.
Collaborate with teams to convert complex requirements into technical solutions.

Requirements

What you’ll need

Strong platform and data infrastructure experience.
Proven track record of scoping and delivering end-to-end data pipelines, tools, and models in production.
Advanced SQL skills for data transformation and querying.
Strong Python skills and familiarity with CI/CD practices.
Experience building pipelines and models on a cloud data warehouse.
Data quality and ownership mindset with a focus on testing and monitoring.
Confidence in using AI tools for analytics interfaces.

Benefits

Comp & perks

Data is treated as a product.
Privacy, security, and responsible data use are prioritized.
Opportunities to experiment with AI and technology improvements.
Involvement in early scoping, technical design, release, monitoring, and iteration.
Collaborative environment across data, product, and engineering teams.