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Tier-1 brand, metro location, mid-level generalist title, and broad tech stack create high competition.
Core data engineering skills transfer across industries, but security-focused telemetry and DevSecOps increase domain specificity.
Mandatory 5–8 years plus required AWS, PySpark, Python, Terraform and DevOps tools makes selection highly strict.
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Lead development and maintenance of scalable AWS-based data pipelines and data platforms to support Group Security and AI/ML use cases.
Own technical strategy and roadmap for security data platform including integration of telemetry and other data assets.
Drive DevOps practices such as CI/CD, automation, infrastructure-as-code, and ensure data quality, reliability, and security across data engineering lifecycle.
5-8 years of data engineering experience in data-intensive environments.
Strong proficiency in AWS (EC2, S3, Lambda, Athena, Kinesis, Redshift, Glue, EMR, DynamoDB, IAM, SecretManager, Step Functions, SQS, SNS, CloudWatch) and Python framework development mandatory.
Experience with big data processing using PySpark/Spark, SQL proficiency, distributed data processing, streaming data architectures, and DevOps tools such as Terraform, Jenkins, Docker, Kubernetes.
Bachelor's or Master's degree in Engineering (Computer Science, Information Technology or relevant).
Experienced AWS Data Engineer specialized in complex automation and framework development within security or data-sensitive environments.
Proven ability to independently own end-to-end data engineering lifecycle including requirements gathering, design, build, test, deployment, and support within DevSecOps.
Demonstrated expertise in integrating AI/ML models into production data pipelines and familiarity with data security best practices including encryption methodologies.
