DingGo is on a mission to modernise how Australia’s automotive ecosystem handles vehicle damage and repair — from first notice of loss through to repair completion. Our market‑leading, data‑driven platform serves panelbeaters, fleet operators, and insurers with tools that streamline the end‑to‑end crash management process through workflow automation and crash intelligence that helps fleets, insurers, and repairers make smarter, faster decisions. We’re a fast‑growing, product‑led company with deep domain expertise and ambitious plans for AU/NZ and beyond. We’re looking for a technically strong Senior Data & Analytics Engineer to become the owner of DingGo’s Data Hub — the backbone of everything from customer‑facing reporting and internal analytics to AI model training and real‑time agentic automation workflows. You’ll work closely with our Head of Data/AI & New Products, CPO Data Analyst, ML Engineer, and AI Automation Manager to ensure clean, well‑modelled, and timely data is available across all of DingGo’s products and pipelines. This is a high‑ownership, hands‑on role. You’ll be both the architect and the builder: defining the target‑state data architecture, executing the roadmap, and keeping the data platform secure, cost‑effective, and compliant with our ISO 27001 obligations. Snowflake dbt Fivetran SQL Python Supabase Document AI / OCR Data Governance ISO 27001 Key Responsibilities Partner with the Head of Data/AI & New Products and CTO to define the Data Hub target‑state architecture and data strategy roadmap. Manage and optimise the total cost of ownership (TCO) across the data platform, including third‑party data sources and tooling licences. Own data governance and security — data catalogue, data policies, network policies, and access controls — ensuring ongoing ISO 27001 compliance. Own, design, and continuously optimise DingGo’s core data models across repair, damage, vehicle, incident, and insurance domains. Operationalise structured ETL and transformation pipelines (e.g. customer vehicle data imports, repair cost feeds). Design and maintain external data acquisition and landing zones for third‑party data sources. Build and maintain data pipelines for PDF classification and extraction using AI‑OCR and Document AI tooling. Build and maintain data pipelines for image classification leveraging Computer Vision and Visual Language Models. Data Pipelines for Reporting & New Products Collaborate with the CPO to ensure accurate, timely data is available for customer‑facing reporting embedded in DingGo’s products, and support new data model development for emerging products. Collaborate with the Data Analyst to power self‑serve and ad‑hoc internal analytics, and support the development of FleetGPT — DingGo’s natural‑language fleet. Data Pipelines for AI Products & Automation Workflows Collaborate with the ML Engineer to ensure high‑quality data is available for AI model training, evaluation, and deployment, with full observability and logging in place. Collaborate with the AI Automation Manager to deliver reliable real‑time data feeds for agentic AI automation workflows, with effective observability and log capture. Immediate Priorities (First 90 Days) 1 — ETL Modernisation Audit current Fivetran‑based sync pipelines and identify gaps for real‑time use cases. Design and implement a live‑connection architecture on Snowflake to support both scheduled reporting and real‑time AI automation workloads. Rationalise or extend Fivetran connectors to reduce latency and cost. Evaluate and select a dedicated BI/visualisation tool to replace Snowflake dashboards and Streamlit (candidates may include AWS QuickSight, Metabase, Sigma, or similar). Implement row‑level security (RLS) and role‑based access control (RBAC) for both customer‑facing and internal dashboards. Migrate existing reports and deliver a scalable reporting layer for the CPO and Data Analyst. Audit and tighten data access controls across all downstream service accounts and Snowflake integration points, ensuring least‑privilege access is enforced and aligned with DingGo’s ISO 27001 controls. Migrate file storage for PDFs and images off EFS and into S3, establishing a structured bucket architecture with appropriate access policies for pipeline and application consumers. Move historical and infrequently accessed documents into S3 Glacier or equivalent cold storage, reducing storage costs while maintaining retrieval capability for compliance and audit purposes. Required Qualifications & Experience A Bachelor’s degree or higher qualification required. 5+ years in a data engineering, analytics engineering, or data platform role, with at least 2 years operating at a senior or lead level. Demonstrable end‑to‑end ownership of a data warehouse or lakehouse environment, including architecture decisions, not just implementation. Advanced Snowflake skills: warehouses, data sharing, Cortex AI, RBAC, network policies, and performance tuning. Proficiency in dbt: modelling, testing, documentation, and CI/CD integration. Solid SQL skills: complex transformations, window functions, CTEs, and JSON/semi‑structured data. Python proficiency for pipeline scripting, automation, and data wrangling. AWS experience across S3, EFS, Athena, and QuickSight; familiarity with streaming and CDC concepts for real‑time pipeline design. Experience operationalising data pipelines that feed Computer Vision, NLP, or VLM. Familiarity with AI OCR, Document AI, or similar PDF intelligence tooling. Experience with data catalogue tooling and data masking, encryption, and audit. Proven ability to work directly with product managers, ML engineers, and data scientists in a product‑led environment — translating business needs into data. Comfortable operating with autonomy in a fast‑moving product environment — you can set direction, manage ambiguity, and deliver without close supervision. Why DingGo? Own a critical, high‑visibility function in a fast‑growing Australian tech company. Work across cutting‑edge AI/ML, agentic automation, and real‑time data use cases — not just traditional BI. Small, talented team — your work has immediate, tangible impact on the product and the business. Competitive salary and flexible hybrid working arrangements based in Sydney. Ambitious plans for AU/NZ expansion and beyond — grow with the company. If you’re a data engineer who wants to shape a modern data platform at the intersection of AI, automation, and automotive technology — we’d love to hear from you. #J-18808-Ljbffr