Data Engineer

• Serve as the technical platform owner for our modern Data & AI platform.
• Lead the design, implementation and evolution of data engineering solutions that enable real-time data processing, IoT integration, and advanced analytics capabilities across the organisation.
• Drive innovation through Generative AI and Machine Learning capabilities, establishing frameworks and pipelines that enable predictive analytics, intelligent automation and data-driven decision making to transform patient care and operational efficiency.
• Enable data democratisation by building robust, user-friendly data pipelines and self-service analytics capabilities that empower stakeholders across the organisation to access and leverage data insights independently and securely.
• Design and implement cloud-native data architectures including data lakes, lakehouses, streaming platforms and interoperability layers.
• Establish data modelling standards, ETL/ELT processes and data quality frameworks that support scalable, reliable data operations.
• Build strong relationships with clinical, operational and technical stakeholders to understand data needs, gather requirements and implement process change management.
• Act as a trusted advisor on data engineering best practices and innovation opportunities.
• Ensure the Mission, Vision, Creed, and Values of SVHA (Compassion, Justice, Integrity, Excellence) are effectively integrated into all areas of responsibility, maintaining ethical data practices and patient-centered technology solutions.

Data Engineering Lead

• Designing, creating and managing the cloud databases and data pipelines that underpin our decoupled cloud architecture and API first approach.
• Take ownership and accountability of functional and non-functional design.
• Collaborate with the People Analytics team to deliver data products needed to support business goals.
• Use deep understanding of data modelling and data quality to identify, transform and validate data required for advanced analytics and machine learning.

Data Engineer, AWS Cloud

• Acting as a lead data authority — owning data quality, reliability, and accuracy across our internal analytics platform, and setting the standard for how we work with data at Tracksuit.
• Managing, scaling, and continuously enhancing our core Analytics data platform (Snowflake + dbt) as Tracksuit expands globally.
• Maintaining and enhancing our data integrations with internal and external systems (including HubSpot, Subskribe, Vitally, and our product database), ensuring seamless data flow and accessibility.
• Designing and building self-service data products that meet complex internal business requirements across Finance, GTM, and Product — reducing reliance on ad-hoc requests and enabling faster decisions.
• Identifying and delivering high-value analytics capabilities for internal stakeholders, translating business problems into well-modelled, trustworthy data.
• Mentoring junior team members, establishing robust data engineering practices, and helping shape the future of our growing global data capabilities.

Senior Data Engineer

• Use your data & analytics industry experience and IT architectural knowledge to ensure Equifax's data platforms are fit for purpose, future-proof, and fully optimized.
• Manage one or more key processes in the data product pipeline with a deep, end-to-end understanding of data product streams.
• Work closely with cross-functional teams both locally and across geographies to create alignment, understand best practices and define long-term product roadmaps and requirements.
• Contribute to the planning and delivery of key processes in the data platform and data product pipeline, including communication of delivery status, risks and change control to senior management.
• Translate complex data platform and data product concepts into clear, impactful technical communications for internal stakeholders.
• Participate in, contribute to, and help shape new data product initiatives across the business.

Senior Data Platforms Analyst

• Supporting end‑to‑end project delivery, from analysis through to client engagement and measurable outcomes.
• Translating technical data and insights into clear, actionable advice for clients.
• Contributing to solutions in areas such as asset management strategies, asset decision-making, performance improvement, system design, and asset condition assessments.
• Collaborating with senior advisors on initiatives spanning operational readiness, reliability engineering, productivity improvement, capital portfolio management and complex, multi-disciplinary projects.

Data Engineer – Asset Management Consultant

• Lead completion of the target data architecture required for Phase 1 EDP implementation and near-term expansion.
• Define shared conformed dimensions, canonical entities, surrogate key strategy, and crosswalk rules across priority datasets.
• Define fact and event models, point-in-time reporting strategy, and reusable analytical outputs to support corporate reporting, operational reporting, and workload forecasting.
• Define bronze, silver, and gold standards by dataset and ensure the role of each layer is clear, consistent, and implementable.
• Prepare implementation-ready dataset packs for priority sources, including source profile, schema, refresh pattern, mappings, business rules, data quality rules, access constraints, retention/destruction requirements, lineage needs, and acceptance criteria.
• Define cross-dataset integration patterns, including relationship modelling and entity resolution inputs and outputs.
• Define architecture requirements for secure self-service access, search, data export, and reusable data products in alignment with role-based access, masking, and audit requirements.
• Review the System Integrator’s solution designs, dataset designs, build artefacts, release artefacts, environment/resource management plans, and operational procedures to ensure alignment with approved architecture and EDP requirements.
• Define architecture guardrails for environments, DevOps, modular pipeline design, reusable components, schema evolution, and non-disruptive delivery.
• Support migration design for priority existing workflows, dashboards, and reports into the EDP.
• Support transition and decommission architecture for migrated workflows and legacy data processes, including archive and dependency considerations.
• Support service transition and production support readiness by ensuring architecture artefacts and delivered data products are supportable, documented, and sustainable.
• Produce architecture artefacts, standards, and documentation required for delivery, acceptance, and ongoing sustainment.
• Support knowledge transfer, handover, and internal capability uplift so the delivered platform can be sustainably operated and evolved.

Data Architect, Databricks

• Lead end-to-end delivery of scalable data platforms and data warehouse solutions
• Design and implement robust data architectures using Snowflake, AWS, Azure, and/or Databricks
• Provide hands-on technical leadership across projects, setting standards and guiding delivery teams
• Build, optimise, and maintain data pipelines, models, and storage layers to ensure reliable data outcomes
• Collaborate with data governance and reporting stakeholders to align solutions with broader data strategy
• Support pre-sales activities by contributing to solution design, scoping, and client proposals
• Work closely with data scientists, analysts, and business stakeholders to deliver impactful solutions
• Translate complex technical concepts into clear, actionable insights
• Present solutions and progress confidently to both technical and non-technical audiences
• Partner with project managers to ensure delivery is on track, within scope, and aligned to expectations

Lead Data Engineering Consultant

• Stay ahead of the latest AWS Cloud, Gen AI, Data Lake, and Data Mesh technologies.
• Build state-of-the-art data solutions that power seamless experiences.
• Design and build next-gen data platforms and pipelines.
• Integrate data from diverse sources into centralised lakes and cloud platforms.
• Create technology blueprints and engineering roadmaps for long-term transformation.
• Ensure data security, governance, and compliance.
• Deliver end-to-end solutions that meet business needs and minimise risk.
• Build high-quality, scalable data products from scratch.
• Communicate effectively with stakeholders and contribute to team-wide engineering practices.
• Drive adoption of catalogues, glossaries, and stewardship practices.

Staff Data Engineer, AWS Cloud, AI

• Drive end-to-end delivery of modern, scalable data platform and warehouse solutions for clients.
• Design and architect robust data ecosystems using technologies such as Snowflake, AWS, Azure, and Databricks, ensuring performance, scalability, and maintainability.
• Provide strong hands-on technical leadership—guiding teams, setting engineering standards, and mentoring consultants to uplift capability across projects.
• Engineer, optimise, and govern data pipelines, models, and storage layers to support high-quality, reliable data outcomes.
• Partner closely with Data Governance and Reporting Principals to align architecture with enterprise data strategy and best practices.
• Contribute to solution design, scope technical engagements, and support pre-sales activities by shaping pragmatic, value-driven client proposals.
• Work collaboratively with cross-functional teams including data scientists, analysts, and business stakeholders to deliver impactful solutions.
• Translate complex technical concepts into clear, meaningful insights for both technical and non-technical audiences.
• Present confidently to clients and internal stakeholders through structured, concise reports and storytelling.
• Collaborate with project managers to ensure delivery remains on track, within scope, and aligned to budget expectations.

Principal Data Engineer

• Define, govern and evolve nbn’s enterprise‑wide ontology and data models
• Provide leadership across all major business domains
• Ensure semantic consistency and alignment with enterprise data modelling practices
• Oversee ontology and data model governance, quality assurance, and standards
• Enable digital‑twin and advanced analytics use cases
• Maintain alignment with industry standards

Principal Data Architect

• Provide senior technical leadership and people management within a high-performing data and geospatial delivery environment.
• Ensure operational performance, quality, and reliability of data engineering and GIS production services.
• Combine practitioner-level technical depth with line management responsibility.
• Provide day-to-day line management, leadership, and professional development for Data Engineers and GIS Production Engineers.
• Foster a high-performing, collaborative team culture focused on quality, accountability, and continuous improvement.
• Oversee workload planning, prioritisation, and delivery outcomes.
• Provide senior technical leadership across geospatial data engineering and GIS production workflows.
• Review and assure technical outputs and act as escalation point for complex issues.
• Drive continuous improvement, capability uplift, and operational maturity through mentoring, standards development, and stakeholder engagement.

Data Engineering Team Lead

• Contribute to the delivery of enterprise geospatial dataflows and analytics pipelines supporting mission-critical outcomes within secure and complex environments
• Design, develop, and maintain geospatial data pipelines and workflows under senior guidance
• Implement data ingestion, transformation, validation, and delivery for geospatial and spatiotemporal datasets
• Develop and manage dataflows using tools such as Apache NiFi, FME, and SQL-based transformations
• Support geospatial ETL processes, including schema management, data validation, and preparation
• Troubleshoot dataflow issues and support integration with downstream systems, analytics, and reporting platforms
• Ensure data quality, documentation, and continuous improvement while collaborating within teams and mentoring junior members

• Provide senior technical leadership and accountability for geospatial data engineering delivery, ensuring quality, compliance, and schedule outcomes.
• Own end-to-end performance of geospatial dataflows, including ingestion, transformation, validation, and delivery to downstream systems.
• Lead the design and implementation of enterprise geospatial dataflows using tools such as Apache NiFi and FME.
• Develop and manage complex geospatial ETL workflows, data models, and embedded data quality controls.
• Act as a technical escalation point, driving root cause analysis and resolution of complex data and pipeline issues.
• Drive continuous improvement, governance alignment, documentation, and mentoring to uplift team capability and engineering practices.

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Data Engineering Lead

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Lead Data Engineering Consultant

Staff Data Engineer, AWS Cloud, AI

Principal Data Engineer

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Data Engineering Team Lead

Data Engineer

Senior Data Engineer