Apply for Senior Data Engineer - AWS and Snowflake at Capgemini in Melbourne, VIC, AU. This full‑time on‑site position offers great opportunities for career growth.

About the Service Line

Capgemini Global Insights & Data business line is a market leader in Data Engineering, Cloud Data Platforms, Data Science, and AI and Advanced Analytics across all sectors including financial services, public sector, consumer products, telecommunication & energy resources.

Responsibilities

- Design, develop, and maintain scalable real‑time, event‑driven, CDC‑based, and batch data pipelines on AWS and Snowflake to support analytics.
- Build and optimize data ingestion frameworks leveraging streaming technologies, event‑based architectures, and Change Data Capture (CDC) patterns.
- Develop and manage cloud‑native data solutions preferably AWS that ensure high availability, reliability, scalability, and performance.
- Optimize Snowflake data models, queries, workloads, and storage strategies to improve performance and cost efficiency.
- Implement and maintain robust data quality, data governance, security, privacy, and compliance controls across the data platform.
- Design and manage ETL/ELT processes for integrating data from multiple internal and external sources.
- Automate infrastructure provisioning, testing, deployment,



and monitoring using CI/CD pipelines and Infrastructure‑as‑Code practices.
- Collaborate closely with solution architects, application development teams, data scientists, business analysts, and key stakeholders to deliver data solutions aligned with business objectives.

Qualifications

- Solid hands‑on experience with Python and SQL for data engineering, transformation, and automation.
- Extensive experience designing and implementing data solutions on AWS, including services such as S3, Glue, Lambda, EMR, Kinesis, EventBridge, Redshift, Step Functions, and related data services.
- Strong expertise in Snowflake, including data modelling, performance tuning, workload optimization, security, and administration.
- Experience building real‑time streaming, event‑driven, and CDC‑based data ingestion architectures.
- Solid understanding of ETL/ELT frameworks, distributed data processing, and modern data engineering patterns.
- Experience with DevOps practices, CI/CD pipelines, version control systems (e.g., Git), and infrastructure automation.
- Knowledge of data governance, data quality frameworks, metadata management, security, and compliance requirements.
- Experience working with large‑scale structured and unstructured datasets in cloud environments.

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📌 Senior Data Engineer - AWS and Snowflake at Capgemini (Melbourne)
🏢 Capgemini
📍 Melbourne