Data Engineer III

About the role

Data Engineer III will design, build and maintain scalable data pipelines and platforms on AWS. The role supports both batch and real‑time data integration, ensuring data is secure, governed and readily available for analytics and data science teams.

Key responsibilities

• Develop ETL/ELT pipelines using AWS Glue, Lambda, Step Functions and orchestrate workflows with Glue Workflows or Step Functions.
• Implement CI/CD pipelines for infrastructure as code using CloudFormation, Terraform or CDK.
• Manage structured and unstructured data in S3, Glue Data Catalog, DynamoDB, RDS/Aurora and Redshift, handling partitioning, indexing and lifecycle policies.
• Integrate data from APIs, on‑prem databases and third‑party sources, leveraging Kinesis, Kafka or Glue for streaming and batch loads.
• Optimize query and job performance on Athena, Databricks or Spark, and choose efficient storage formats such as Parquet or ORC.
• Apply security best practices including encryption, IAM roles, Lake Formation policies and ensure GDPR compliance.
• Monitor pipeline health with CloudWatch, CloudTrail and custom dashboards, set up alerts for failures and anomalies.
• Collaborate with data scientists, analysts and BI teams in Agile ceremonies.

Required profile

• Minimum 3 + years of professional experience in data engineering or software engineering with a strong data focus.
• Proven ability to deliver production‑grade data pipelines across multiple domains.
• Experience translating ambiguous business requirements into scalable, governed data solutions.
• Comfort working in Agile, cross‑functional teams alongside product, analytics and data‑science partners.
• Bachelor’s degree in Engineering, Computer Science or equivalent practical experience.

Required skills

• AWS services: S3, Glue, Redshift, Athena, EMR, Lambda, CloudFormation, Step Functions, Kinesis, DynamoDB, SQS, SNS, Lake Formation.
• Programming languages: Python, SQL, Scala.
• Big‑data technologies: Apache Spark, Databricks, Hadoop, Kafka.
• Infrastructure‑as‑code and DevOps tools: Terraform, CDK, Git, CI/CD pipelines.
• Data security and governance concepts, including encryption and IAM.