Security Architect
SQA Solution
As a Senior DevSecOps Engineer, you will play a key role in designing, building, and maintaining production-grade data and platform pipelines and infrastructure within our AWS-based data lakehouse ecosystem. Working alongside data engineers and other engineering teams, you will operationalize workloads and ensure the reliability, security, and scalability of the platform lifecycle, from data ingestion through deployment and monitoring.
You will help shape our DevSecOps framework, contribute to automation that accelerates delivery, and ensure alignment with established platform Non-Functional Requirements (NFRs). This is a highly collaborative, hands-on engineering role requiring a deep understanding of AWS services, automation, and workflow orchestration, along with a strong commitment to software development best practices.
This position is remote and operates within a distributed agile environment.
Design, build, and maintain automation pipelines supporting workload packaging, validation, and deployment across AWS environments.
Implement automation for packaging, testing, deployment, and monitoring using CI/CD best practices.
Collaborate with data engineers and other engineering teams to operationalize workloads within the data lakehouse ecosystem.
Develop and maintain integrations between data ingestion, storage, and downstream service repositories.
Apply infrastructure-as-code (Terraform, AWS CDK, CloudFormation) to build reusable, composable constructs and modules that are version-controlled and validated through automated tests, rather than one-off scripts glued together.
Implement and manage versioning, reproducibility, and lineage tracking for pipeline artifacts and configurations.
Define and automate monitoring, alerting, and remediation strategies for deployed services.
Ensure all infrastructure and pipelines meet enterprise security, compliance, and governance standards.
Participate in code reviews, knowledge sharing, and continuous improvement of DevSecOps practices.
Mentor junior engineers and contribute to documentation, standards, and best practices for software delivery across teams.
This role is part of the Data & AI organization, focusing on the operationalization of pipelines and platform infrastructure within AWS. Areas of specialty include:
Versioning, governance, and observability
Testable, modular infrastructure-as-code practices
Secure, compliant, and scalable platform infrastructure
Continuous improvement of lifecycle automation
6+ years of professional experience in software or data engineering.
3+ years of direct experience implementing and maintaining production pipelines and infrastructure.
Strong proficiency in Python and solid software engineering fundamentals (testing, code review, version control).
Hands-on experience with AWS services (Step Functions, Lambda, ECR, S3, Glue, IAM, CloudWatch).
Solid understanding of CI/CD, containerization (Docker)
Experience with building CI/CD pipelines (Jenkins, Github Actions, etc.).
Experience building reusable, composable infrastructure-as-code (Terraform, AWS CDK, or CloudFormation) as true, testable constructs, not just automation scripts.
Strong understanding of data pipelines, ETL/ELT concepts, and data modeling in a lakehouse environment.
Proven ability to apply software engineering best practices, including version control, automated testing, and code review, to infrastructure and data workflows.
Strong communication and collaboration skills across multidisciplinary teams.
Experience with data catalogs and metadata management.
Familiarity with data governance and compliance frameworks.
Experience with observability and monitoring tooling (CloudWatch, or custom solutions).
Understanding of data lakehouse technologies such as Apache Iceberg or Delta Lake.
Contributions to open-source DevOps tooling.
Experience in Agile development environments and cross-functional collaboration.
Originally posted on Himalayas
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As a Senior DevSecOps Engineer, you will play a key role in designing, building, and maintaining production-grade data and platform pipelines and infrastructure within our AWS-based data lakehouse ecosystem. Working alongside data engineers and other engineering teams, you will operationalize workloads and ensure the reliability, security, and scalability of the platform lifecycle, from data ingestion through deployment and monitoring.
You will help shape our DevSecOps framework, contribute to automation that accelerates delivery, and ensure alignment with established platform Non-Functional Requirements (NFRs). This is a highly collaborative, hands-on engineering role requiring a deep understanding of AWS services, automation, and workflow orchestration, along with a strong commitment to software development best practices.
This position is remote and operates within a distributed agile environment.
Design, build, and maintain automation pipelines supporting workload packaging, validation, and deployment across AWS environments.
Implement automation for packaging, testing, deployment, and monitoring using CI/CD best practices.
Collaborate with data engineers and other engineering teams to operationalize workloads within the data lakehouse ecosystem.
Develop and maintain integrations between data ingestion, storage, and downstream service repositories.
Apply infrastructure-as-code (Terraform, AWS CDK, CloudFormation) to build reusable, composable constructs and modules that are version-controlled and validated through automated tests, rather than one-off scripts glued together.
Implement and manage versioning, reproducibility, and lineage tracking for pipeline artifacts and configurations.
Define and automate monitoring, alerting, and remediation strategies for deployed services.
Ensure all infrastructure and pipelines meet enterprise security, compliance, and governance standards.
Participate in code reviews, knowledge sharing, and continuous improvement of DevSecOps practices.
Mentor junior engineers and contribute to documentation, standards, and best practices for software delivery across teams.
This role is part of the Data & AI organization, focusing on the operationalization of pipelines and platform infrastructure within AWS. Areas of specialty include:
Versioning, governance, and observability
Testable, modular infrastructure-as-code practices
Secure, compliant, and scalable platform infrastructure
Continuous improvement of lifecycle automation
6+ years of professional experience in software or data engineering.
3+ years of direct experience implementing and maintaining production pipelines and infrastructure.
Strong proficiency in Python and solid software engineering fundamentals (testing, code review, version control).
Hands-on experience with AWS services (Step Functions, Lambda, ECR, S3, Glue, IAM, CloudWatch).
Solid understanding of CI/CD, containerization (Docker)
Experience with building CI/CD pipelines (Jenkins, Github Actions, etc.).
Experience building reusable, composable infrastructure-as-code (Terraform, AWS CDK, or CloudFormation) as true, testable constructs, not just automation scripts.
Strong understanding of data pipelines, ETL/ELT concepts, and data modeling in a lakehouse environment.
Proven ability to apply software engineering best practices, including version control, automated testing, and code review, to infrastructure and data workflows.
Strong communication and collaboration skills across multidisciplinary teams.
Experience with data catalogs and metadata management.
Familiarity with data governance and compliance frameworks.
Experience with observability and monitoring tooling (CloudWatch, or custom solutions).
Understanding of data lakehouse technologies such as Apache Iceberg or Delta Lake.
Contributions to open-source DevOps tooling.
Experience in Agile development environments and cross-functional collaboration.
Originally posted on Himalayas