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Job Description
Structured overview of role & requirementsAbout This Role
Design, develop, and optimize scalable AWS-based ETL/ELT data pipelines and platforms using services like Glue, Lambda, Step Functions, and storage technologies including S3, Redshift, and DynamoDB.
Manage large structured and unstructured datasets ensuring data security, governance, and compliance with policies such as GDPR using AWS Lake Formation and IAM.
Monitor pipeline health and performance through tools like CloudWatch and CloudTrail, and collaborate in Agile teams to meet evolving business data needs.
Minimum Requirements
3+ years of professional experience in data engineering or software engineering with a strong data focus.
Experience with AWS services (Glue, Lambda, Step Functions, S3, Redshift, Kinesis, DynamoDB, Lake Formation), Python and SQL, and big data tools (Spark, Databricks, Hadoop, Kafka).
Proven track record delivering complex production-grade data pipelines with knowledge of DevOps tools (Git, CI/CD, CloudFormation or Terraform).
Bachelor's degree in Engineering or Computer Science preferred, or equivalent experience.
Ideal Candidate Profile
Experienced in managing very large data warehouses or data lakes with a focus on secure, governed, and compliant data architectures.
Adept at translating ambiguous business and analytical requirements into scalable, automated data solutions within Agile cross-functional teams.
Comfortable with DevOps practices integrating automation (CI/CD pipelines, infrastructure as code) and capable of troubleshooting complex data processing workflows.