Senior Data Engineer AWS

Partner with organisations to design and deliver end-to-end enterprise data platforms and unlock the full potential of their analytics capabilities At Maltem , we are a global consulting firm with over 1,200 experts across 12 countries, specialising in data, digital transformation, and innovation. We partner with major clients across industries to deliver high-impact technology solutions. In Australia, we’re growing rapidly and looking for passionate professionals to join us in leading data‑driven projects. We are currently seeking a Data Engineer – Databricks & ML to join a major client based in Melbourne. Data Engineer – Databricks & ML | Melbourne-Based | Hybrid An exciting opportunity for an experienced Data Engineer to work on large‑scale, cloud-native data projects using Databricks and to contribute to the operationalisation of Machine Learning solutions. Key Responsibilities Databricks Platform Enablement: Set up and manage Databricks clusters, jobs, and workflows for data engineering and machine learning. Enable analytics and data science teams to leverage Databricks for ML model development and deployment. Pipeline Development & Orchestration: Design, develop, and maintain scalable data pipelines using Databricks and AWS services. Implement orchestration for ETL, feature engineering, and ML workflows. ML Workflow Integration: Use MLflow for experiment tracking, model versioning, and deployment. Support end‑to‑end ML lifecycle, including model training, deployment, and monitoring. Data Quality & Governance: Apply data validation, quality checks, and security best practices across all pipelines and ML workflows. Performance Optimization: Tune Spark jobs and Databricks clusters for efficiency and cost optimisation. Monitoring & Troubleshooting: Proactively monitor Databricks jobs, ML models, and AWS pipelines, ensuring timely resolution of issues. Collaboration: Work closely with data scientists, analysts, and business stakeholders to deliver robust ML‑ready data solutions. Continuous Improvement: Stay updated on Databricks, AWS, and ML engineering best practices to drive innovation and efficiency. Required Skills & Experience Experience: 5+ years in data engineering, with hands‑on experience in Databricks and AWS environments. Cloud Expertise: Strong proficiency in AWS services (S3, Glue, Lambda, Redshift, IAM). Exposure to Azure data services is a plus. Databricks Skills: Experience with cluster setup, job orchestration, and ML workflows on Databricks. Familiarity with Delta Lake for data reliability and performance. Programming: Proficiency in Python and/or Scala; strong Spark experience required. ML & Data Knowledge: Understanding of machine learning workflows, feature engineering, and model deployment pipelines. Experience with MLflow for model tracking and deployment. Database Skills: Strong SQL skills; experience with NoSQL databases is desirable. DevOps: Knowledge of CI/CD pipelines and automation for data workflows. Certifications: AWS Certified Data Analytics – Specialty or AWS Solutions Architect; Databricks Machine Learning Professional Certification (or equivalent) highly desirable. What We Offer Join a fast‑growing consulting team with real impact Work with modern, in‑demand technologies Attractive daily rate and long‑term project visibility Supportive environment with expert communities Work Arrangement Hybrid: 3 days onsite, 2 days remote Job Details Seniority level: Mid‑Senior level Employment type: Contract Job function: Information Technology Industries: IT Services and IT Consulting #J-18808-Ljbffr