Senior DevOps Engineer

• GitLab CI/CD pipeline design and implementation
• Terraform modules for AWS and Databricks infrastructure
• Databricks Asset Bundles for Databricks deployment automation
• Databricks workspace configuration and environment promotion
• Dev, QA, QA2, Prod, and DR deployment patterns
• AWS S3, IAM, KMS, STS, VPC endpoints, PrivateLink, and cross-account access
• Databricks Unity Catalog permissions and governance automation
• Databricks workflows, jobs, notebooks, SQL warehouses, and cluster policies
• Release tagging, deployment approvals, rollback procedures, and release validation
• Monitoring for Databricks jobs, workflows, batch SLAs, and platform usage
• Operational runbooks for AMS, platform support, and engineering teams
• Secure access patterns for data ingestion, data products, and enterprise integrations

Ø Complex work environment with heavy interpersonal & technical demands Ø Requires multi-tasking and coordination across multiple geographic locations

QUALIFICATIONS & EXPERIENCE:

• Bachelor's degree or equivalent experience in Computer Science, Information Technology, Engineering, Data Engineering, or a related field.

•

.

• Strong hands-on experience with

.

• Strong hands-on experience with

for infrastructure automation.

• Experience with

, especially S3, IAM, KMS, STS, VPC, subnets, security groups, VPC endpoints, and PrivateLink.

• Experience with

.

• Experience implementing or supporting

, or similar Databricks deployment frameworks.

• Good understanding of

.

• Good understanding of

, data access controls, external locations, storage credentials, and governance concepts.

• Strong scripting experience using

.

• Good understanding of

.

• Experience supporting production platforms with monitoring, alerting, logging, incident management, and operational runbooks.
• Strong troubleshooting skills across cloud, network, security, CI/CD, and data platform layers.
• Experience working in Agile delivery models with globally distributed engineering teams.

Experience with one or more of the following would be a strong plus:

• AWS multi-account patterns and cross-account IAM role access.
• AWS S3 data lake architecture, bucket policies, KMS encryption, and lifecycle policies.
• AWS PrivateLink, gateway endpoints, interface endpoints, DNS, and enterprise network routing.
• Databricks on AWS, including workspace deployment, cluster policies, job clusters, serverless, SQL warehouses, system tables, and audit logs.
• Databricks Unity Catalog, Delta Sharing, external locations, storage credentials, and permission automation.
• Databricks Asset Bundles for multi-environment deployment.
• GitLab merge request workflows, CI/CD templates, protected branches, environment approvals, and release tagging.
• Terraform module design, state management, environment parameterization, and reusable IaC patterns.
• Data platform observability using Databricks system tables, cloud logs, dashboards, and SLA reporting.
• Power BI connectivity to Databricks and service-principal-based access patterns.
• MLOps concepts including model deployment, versioning, feature stores, experiment tracking, and ML workflow automation.
• Experience supporting Azure-to-AWS migration or multi-cloud transition programs.

• Proactive and comfortable owning platform improvements end to end.
• Strong in problem solving, debugging, and structured investigation.
• Experienced in building automation rather than relying on manual operations.
• Comfortable working across engineering, architecture, security, infrastructure, and support teams.
• Detail-oriented when it comes to production controls, access, security, and release quality.
• Pragmatic and delivery-focused, with a bias toward simplification and standardization.
• Able to explain technical topics clearly to both technical and non-technical stakeholders.
• Passionate about building reliable, scalable, secure, and well-governed data platforms.

• GitLab CI/CD becomes the standard deployment path for EDP engineering assets.
• Terraform modules are reusable, secure, and consistently applied across environments.
• Databricks Asset Bundles are adopted as the standard for Databricks job and workflow deployments.
• AWS infrastructure and Databricks environments are provisioned consistently and with minimal manual effort.
• Releases become safer, faster, and better governed across Dev, QA, QA2, Prod, and DR.
• Monitoring gives early visibility into job failures, SLA risks, platform issues, and operational bottlenecks.
• Security and access controls are automated, auditable, and aligned with least-privilege principles.
• AMS and engineering teams have clear runbooks, handoffs, and operational ownership models.

• Opportunity to work on a strategic global Enterprise Data Platform.
• Exposure to modern AWS-first data platform architecture with Databricks at the core.
• Opportunity to contribute to a major cloud migration and platform modernization journey.
• A collaborative, international technology environment.
• High visibility across data engineering, architecture, security, infrastructure, and analytics teams.
• Space to drive automation, improve engineering maturity, and shape DevOps standards for the platform.

PVH Corp. or its subsidiary ("PVH") is an equal opportunity employer and considers all applicants for employment on the basis of their individual capabilities and qualifications without regard to race, ethnicity, color, sex, gender identity or expression, age, religion, national origin, citizenship status, sexual orientation, genetic information, physical or mental disability, military status or any other characteristic protected under federal, state or local law. In addition to complying with all applicable laws, PVH is also committed to ensuring that all current and future PVH associates are compensated solely on job-related factors such as skill, ability, educational background, work quality, experience and potential.