DataOps Consultant; Lakehouse

Working model: fully onsite from the client's office.

Employment: official full-time employment through a DXC Legal Entity.

We offer a comprehensive benefits package to support your health, wellbeing, and financial security:

• Visa and relocation support
• Comprehensive medical and life insurance coverage
• Family support benefits
• Education assistance for children
• Additional benefits in line with local company policies

We are hiring a Support Engineer who combines production support expertise with Dev Ops engineering capability and AI fluency.

This role spans the full D&E portfolio: in-house .NET/C# applications, APIs, event-driven systems, data platforms, and BI tools. You will ensure operational stability while continuously improving reliability through automation, observability, and modern Dev Ops practices.

You will work directly alongside Software Engineers as the bridge between development and operations, owning incident resolution, CI/CD pipeline health, infrastructure automation, and production readiness. You will leverage AI tools daily to accelerate triage, automate runbooks, and proactively detect issues before they impact users.

We are an AI-first engineering team: you will use AI-powered observability, automated diagnostics, and intelligent incident management to deliver faster resolution and higher system reliability.

• Coordinate resolution across platform engineering, Data Engineers and data domain teams (Databricks, Snowflake, Palantir);
• Ensure correct usage of ITSM tools (e.g., Service Now) for incident tracking and lifecycle management;
• Monitor critical workflows using Dynatrace;
• Workflows orchestration using Stone Branch;
• Leverage metadata and documentation for troubleshooting and root cause analysis;
• Maintain clear ownership, escalation paths and communication during incidents;
• Reduce incident noise and recurring issues through structured problem management.

• Own L2/L3 production support across in-house applications (.NET/C#, Python), APIs, event-driven systems, and data platforms.
• Lead incident triage, diagnosis, and resolution with structured root cause analysis (RCA) and post-incident reviews.
• Manage incident, problem, and change management processes aligned with ITIL best practices.
• Coordinate with development teams, infrastructure, and vendors for escalation and resolution of complex production issues.
• Maintain and improve production verification suites, canary checks, and reliability playbooks.

• Own and maintain CI/CD pipelines in Azure Dev Ops: build, test, deploy, and release automation for both application and data platform projects.
• Infrastructure as Code:
  Terraform, Bicep, or ARM templates for provisioning and managing Azure cloud resources.
• Container orchestration:
  Docker and Kubernetes (AKS) for deploying, scaling, and troubleshooting containerized services.
• Environment management: provision, configure, and maintain development, staging, and production environments.
• Implement and maintain deployment strategies (blue-green, canary, rolling) to minimize production risk.

• Azure:
  Practical experience with App Services, AKS, Functions, Event Hubs, Storage, Key Vault, Azure SQL, and Azure Monitor.
• Manage cloud resource health, cost optimization, and security posture.
• Support microservice architectures: service discovery, load balancing, scaling, and resilience patterns.
• Networking fundamentals: DNS, load balancers, firewalls, VNets, and private endpoints in Azure.

• Implement and manage monitoring stacks:
  Dynatrace, Azure Monitor or equivalent.
• Build and maintain alerting rules, dashboards, and SLO/SLI tracking for critical services and data pipelines.
• Distributed tracing with Open Telemetry: correlate trace IDs across services, diagnose latency bottlenecks, and validate signal fidelity.
• Data observability: monitor data pipeline health, detect anomalies, latency issues, and data loss using platforms like Monte Carlo Data or equivalent.
• Log aggregation and analysis for rapid incident diagnosis.

• Applications:
  Support .NET/C# backend services, REST/GraphQL APIs, event-driven systems (Solace, Kafka, RabbitMQ), and WPF/web front-ends.
• Data Platforms:
  Support ETL/ELT pipelines (PySpark, Spark), big data platforms (Databricks, Palantir Foundry), and data warehouses.
• Databases:
  Advanced SQL for diagnosing data issues, validating transformations, and supporting…