DataOps Engineer

Job Description :

This is a Contract/ C2H role, Apply only if comfortable.

We are looking for a DataOps Engineer (L3) with 3 - 6 years of hands-on experience building and operating production-grade data platforms at scale. This is not a tooling maintenance or report-writing role - you will help run critical data systems, build for reliability and scalability, and enable smooth, high-velocity data delivery across multi-cloud, multi-environment architectures.

This is a genuinely multi-cloud role. Databricks expertise is mandatory, with Azure as the primary cloud and GCP as a co-primary. Beyond data pipelines, you will work on the underlying cloud infrastructure and networking for the data platform - provisioning compute, storage, and key vaults as code, and designing and troubleshooting VNet/VPC, subnets, peering, private endpoints, DNS, firewalls, and cross-cloud connectivity between Azure and GCP.

You will help operate the Databricks Lakehouse and Unity Catalog, build and run batch and streaming pipelines, contribute to IaC and CI/CD coverage for data, integrate data across Azure and GCP, help run the data observability stack, and take part in incident response from detection through to preventive resolution. Collaboration with engineering, ML, analytics, and the central cloud/network/InfoSec teams is a core part of the role - you will contribute to data and infrastructure decisions that affect every team.

A prior background in software or data engineering is a strong advantage. You should be comfortable writing automation and pipeline code, reviewing deployment configurations, and engaging in technical architecture discussions without needing things simplified. This role is ideal for someone who treats the data platform as a product - one that engineering and analytics teams depend on, and one that must continuously improve in reliability, performance, and cost efficiency.

About the Role :

We are seeking a DataOps Engineer (Level 3) with 36 years of handson experience building and operating productiongrade data platforms at scale. The role focuses on reliability, scalability, and highvelocity data delivery across multicloud environments. It is not a toolingmaintenance or reportwriting position; you will own critical data systems and infrastructure.

Key Responsibilities :

- Operate and enhance the Databricks Lakehouse and Unity Catalog in a multicloud environment (Azure primary, GCP coprimary).

- Design, provision, and manage compute, storage, and key vault resources as code using IaC frameworks.

- Build, run, and maintain batch and streaming data pipelines, ensuring endtoend observability and performance.

- Design, configure, and troubleshoot VNet/VPC, subnets, peering, private endpoints, DNS, firewalls, and crosscloud connectivity between Azure and GCP.

- Contribute to CI/CD pipelines for data workloads, including automated testing and deployment validation.

- Participate in incident response from detection through rootcause analysis and preventive remediation.

- Collaborate with engineering, machinelearning, analytics, cloud, networking, and InfoSec teams to influence data and infrastructure decisions.

- Treat the data platform as a product, continuously improving reliability, performance, and cost efficiency.

Required Skills :

- Strong expertise in Databricks, including Lakehouse architecture and Unity Catalog.

- Handson experience with Azure services (e.g., Azure Data Factory, Azure Synapse, Azure Key Vault) and GCP services (e.g., BigQuery, Cloud Storage, Cloud KMS).

- Proficiency in infrastructureascode tools (Terraform, ARM templates, or similar) and CI/CD frameworks (GitHub Actions, Azure DevOps, Jenkins).

- Solid understanding of networking concepts across cloud providers: VNet/VPC, subnets, peering, private endpoints, DNS, firewalls, and crosscloud routing.

- Strong programming/scripting skills in Python, SQL, and/or Bash for automation and pipeline development.

- Experience with data observability and monitoring tools (e.g., Datadog, Prometheus, OpenTelemetry).

Preferred Skills :

- Background in software engineering or data engineering.

- Familiarity with streaming platforms such as Apache Spark Structured Streaming or Kafka.

- Knowledge of costoptimization strategies for cloud data workloads.

Qualifications :

- 36 years of relevant experience in data platform operations or data engineering.

- Demonstrated ability to work independently on complex technical problems without simplification.

- Excellent communication skills for crossfunctional collaboration and technical architecture discussions.

Benefits :

- Competitive contract rate with potential conversion to fulltime.

- Opportunity to work on cuttingedge multicloud data infrastructure.

- Collaborative environment with engineering, ML, analytics, and security teams.

Interview Process :

- Initial technical screen focusing on data platform fundamentals and cloud networking.

- Handson coding/automation exercise.

- Deepdive interview on architecture, incident response, and multicloud design.

- Final discussion with hiring manager and team leads.

Recruiter Summary :

- Contracttohire DataOps Engineer (L3) with 36 years of production data platform experience.

- Mandatory Databricks expertise; primary cloud Azure, secondary cloud GCP.

- Strong IaC, CI/CD, and crosscloud networking skills required.

- Role emphasizes reliability, scalability, and productmindset for data platforms.

- Collaboration across engineering, ML, analytics, cloud, and security teams.