AWS Data Engineering Advisor
$117.3k - $179.86kFull-time
Engie
What You Can Expect We are looking for an AWS Data Engineering Advisor to build applications and accelerate delivery using Agentic AI coding. As the Data Engineering Advisor, you will report to Senior Data Architect, you will focus on developing production-ready software, integrating AI agents into engineering workflows, and using AWS services to deliver secure, scalable solutions.
- Build and maintain robust ETL/ELT pipelines in Python on AWS
- Develop ingestion workflows from external systems, for example Snowflake and APIs, into S3-based data lake zones
- Implement and optimize data processing with AWS Glue (Spark/PySpark), Athena SQL, and Parquet/Iceberg table patterns
- Create and maintain streaming data workflows using Amazon Kinesis
- Manage schema validation, data quality checks, partitioning strategy, and metadata consistency
- Build reusable utility libraries for AWS interactions, including sessions, Secrets Manager, S3, Athena, and Glue
- Implement config-driven pipeline execution and operational controls using services like DynamoDB
- Own deployment and runtime operations for containerized workloads on AWS Batch and ECS with ECR
- Ensure observability through structured logs, run summaries, retry logic, and alerting hooks
- Partner with analytics, platform, and business teams to deliver reliable datasets and SLA-backed pipelines
- Bachelor's degree in Computer Science, Information Technology, MIS, or a related discipline, or an equivalent combination of education and relevant experience
- A minimum of 7 years of data engineering experience, with strong AWS implementation depth
- Strong Python skills for data engineering and pipeline development
- Hands-on experience with S3, Glue, Athena, IAM, Secrets Manager, and CloudWatch
- Experience with Spark/PySpark and large-scale data processing
- Experience writing and optimizing SQL for analytical workloads
- Experience with Parquet-based lake design, partitioning, and performance tuning
- Experience with containerized data workloads (Docker) and orchestration on AWS Batch and ECS
- Familiarity with streaming concepts and Kinesis-based ingestion
- Strong software engineering practices, including modular code, testing, code reviews, and CI/CD awareness
- Ability to troubleshoot production data pipelines and drive root-cause resolution
- This role is eligible for our hybrid work policy; a minimum of 3 days working in office per week
- Must be willing and able to comply with all ENGIE ethics and safety policies;
Vacancy posted a month ago
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