Job description

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 What You’ll Bring • 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 Additional Details • 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; Compensation Salary Range: $117,300 – $179,860 USD annually This represents the average expected pay range for a qualified candidate. ENGIE complies with all federal, state, and local minimum wage laws. Actual salary offered may vary depending on geography, experience, education, internal pay alignment, or other bona fide factors. In addition to base pay, this position is eligible for a competitive bonus / incentive plan. Your Talent Acquisition Partner can share more specific information regarding the benefits or the salary for the position based on the work location. At ENGIE, we take your well-being seriously. Our comprehensive benefits package includes options for medical, dental, vision, life insurance, employer-paid short-term and long-term disability insurance, ESPP, generous paid time off including wellness days, holidays and leave programs. We also help you plan for retirement by offering a 401(k) Retirement Savings Plan with a company match. But that's not all – we're dedicated to the health and happiness of your entire family, offering supplemental benefits for full time employees that enhance emotional and physical well-being through all stages of life from family forming to caregiver benefits. Explore our benefits package to see how we can support you. Learn more . Why ENGIE? ENGIE North America isn’t just participating in the Zero-Carbon Transition, we’re leading it! Join us as we develop energy that is renewable, efficient, and accessible to everyone. At ENGIE, every talent has a role to play in the adventure of the century. Make a difference and enjoy a fulfilling professional experience. Take on exciting challenges and build a career path that reflects who you are. Join us and be part of the adventure! ENGIE is proud to be an equal opportunity workplace, and we are firmly committed to creating an inclusive workplace for all employees. We are committed to providing employees with a work environment free of discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, disability, veteran status, or other legally protected status. If you need assistance with this application or a reasonable accommodation due to a disability, you may contact us at ENGIENA-ENGIEHR@engie.com. This email address is reserved for individuals with disabilities in need of assistance and is not a means of inquiry regarding positions or application status. We value internal mobility and will offer sponsorship for this role to internal candidates only. The safety of our employees is our number one priority. All employees at ENGIE have both a duty and the authority to STOP WORK if unsafe acts are observed.

Requirements

Must-haves

What You Can Expect
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
What You’ll Bring
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
Additional Details

Nice-to-haves

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Keywords to mirror

awsaipythonsqldockerci/cd

Platform: successfactors-unify
Effort: medium

Benefits

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Dental

Vision

401(K)

Paid Time Off

Wellness