This role will be responsible for improving how EDP engineering teams build, test, deploy, monitor, and operate data platform solutions across environments. You will work closely with data engineers, architects, platform teams, security, cloud infrastructure, AMS support teams, and product stakeholders to create reliable, secure, automated, and scalable DevOps practices for EDP.
PRIMARY RESPONSIBILITIES/ACCOUNTABILITIES OF THE JOB
Area
Responsibilities
DevOps & CI/CD
Design, build, and maintain GitLab CI/CD pipelines for EDP data engineering assets, Databricks jobs, workflows, notebooks, SQL assets, and platform configurations.
Infrastructure as Code
Build, maintain, and improve Terraform modules for AWS infrastructure, Databricks workspace configuration, networking, IAM, storage, and platform components.
Databricks Deployment Automation
Implement and standardize Databricks Asset Bundles (DAB) for deploying Databricks workflows, jobs, notebooks, permissions, and environment-specific configurations.
AWS Platform Engineering
Support AWS-native platform components including S3, IAM roles, KMS, STS, VPCs, subnets, security groups, route tables, VPC endpoints, PrivateLink, and cross-account access patterns.
Release Management
Support structured release governance across Dev, QA, QA2, Prod, and DR environments, including release tagging, deployment validation, rollback planning, and production readiness checks.
Monitoring & Observability
Improve monitoring and alerting for Databricks jobs, workflows, pipelines, SQL warehouses, batch SLAs, cloud services, and platform health using Databricks system tables, cloud logs, dashboards, and alerts.
Security & Access Controls
Automate and govern least-privilege access using IAM roles, service principals, Unity Catalog permissions, storage access controls, secrets, KMS encryption, and audit logging.
Network & Connectivity
Support secure connectivity patterns involving Databricks on AWS, S3, PrivateLink, VPC endpoints, enterprise network inspection, DNS, controlled ingress/egress, and cross-cloud transition scenarios.
Operational Excellence
Partner with AMS and engineering teams to improve incident triage, root cause analysis, batch monitoring, SLA visibility, production support handoffs, and operational runbooks.
Engineering Standards
Help define and enforce standards for GitLab workflows, merge request checks, deployment approvals, unit testing, release validation, and reusable deployment patterns.
Platform Modernization
Contribute to EDP’s AWS migration and modernization roadmap, including platform automation, environment consistency, scalable deployment patterns, and improved operational resilience.
SUPERVISORY RESPONSIBILITIES
Direct: NA
BUDGETARY RESPONSIBILITIES
None
Ø RESOURCEFULNESS
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
ENVIRONMENT
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.
6+ years of DevOps, platform engineering, cloud engineering, or data platform engineering experience.
Strong hands-on experience with GitLab CI/CD.
Strong hands-on experience with Terraform for infrastructure automation.
Experience with AWS cloud infrastructure, especially S3, IAM, KMS, STS, VPC, subnets, security groups, VPC endpoints, and PrivateLink.
Experience with Databricks platform engineering or deployment automation.
Experience implementing or supporting Databricks Asset Bundles, or similar Databricks deployment frameworks.
Good understanding of Databricks workflows, jobs, clusters, notebooks, SQL warehouses, permissions, and workspace configuration.
Good understanding of Unity Catalog, data access controls, external locations, storage credentials, and governance concepts.
Strong scripting experience using Python, Bash, or similar scripting languages.
Good understanding of SQL, Spark, Delta Lake, and modern data platform concepts.
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.
Preferred Experience
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.
Key Skills
Skill Area
Expected Capability
Primary Cloud
AWS, S3, IAM, KMS, STS, VPC, PrivateLink, VPC endpoints
DevOps Platform
GitLab CI/CD, merge requests, protected branches, approvals, release tagging
Infrastructure as Code
Terraform modules, state management, reusable IaC patterns
Databricks Deployment
Databricks Asset Bundles, workflows, jobs, notebooks, SQL assets
Data Platform
Databricks, Delta Lake, Spark, Unity Catalog, SQL Warehouses
Security
IAM roles, service principals, KMS, secrets, access controls, auditability
Monitoring
Databricks system tables, cloud logs, dashboards, alerts, SLA tracking
Scripting
Python, Bash, SQL
Operations
Incident triage, RCA, runbooks, release controls, production support
Ways of Working
Agile delivery, documentation, cross-functional collaboration, ownership
You Are
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.
Success in This Role Looks Like
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.
What We Offer
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.
