Senior Data Engineer (AWS)

About the role

We are seeking a highly skilled Senior Data Engineer to join a mission‑critical client project, acting as a bridge between complex business requirements and scalable technical execution. In this role you will collaborate closely with Data Architects, Engineering leads, and stakeholders to design and maintain sophisticated data models that enhance accessibility and governance.

Key responsibilities

• Design and develop efficient, scalable data pipelines between enterprise transactional systems, third‑party sources, and analytics platforms.
• Build and maintain a high‑performance data mesh environment using modern AWS services such as S3, Redshift, EMR, Hive, Presto, Flink and Spark.
• Integrate third‑party data sources and APIs into the data mesh ecosystem and support analytics and data‑science teams.
• Create, modify and monitor ETL processes with AWS Glue, Lambda, Python or Node.js.
• Ensure data governance, quality checks and lineage are implemented at each data hop.
• Collaborate with machine‑learning engineering to support AI products at scale.

Required profile

• 5+ years of experience as a data engineer in an innovative organization.
• 4+ years of hands‑on experience building data lake systems on AWS (S3, Redshift, EMR, Hive, Kafka, Spark).
• Strong knowledge of AWS services (EC2, S3, Route 53, ELB, VPC, CloudWatch, Lambda, DynamoDB, RDS) in multi‑account production environments.
• Proficiency in at least one programming language: Go, Node.js or Python, and solid SQL skills.
• Experience with Agile development practices and ability to deliver incrementally.

Required skills

• Go, Node.js, Python
• SQL (Microsoft SQL Server, PostgreSQL)
• AWS services: S3, Redshift, EMR, Glue, Lambda, EC2, VPC, CloudWatch, DynamoDB, RDS, Route 53, ELB, Step Functions
• Data processing frameworks: Spark, Flink, Hive, Presto, Kafka
• Infrastructure as code: Terraform, CloudFormation
• Machine‑learning libraries: TensorFlow, MLlib, R, SparklyR (optional)