DataOps Consultant (Lakehouse Support)

Luxoft; Abu Dhabi; On-site; AED 260,000 - 420,000; Full time

Benefits offered by this job

Visa and relocation support

Medical and life insurance coverage

Family support benefits

Education assistance for children

Additional benefits in line with local

Job summary

Luxoft in Abu Dhabi, UAE, is hiring a Support Engineer to bridge development and operations. You will own incident resolution, CI/CD health, infrastructure automation, and production readiness for .NET/C#, Python apps and data platforms.

Daily use of AI tools for triage, runbooks, and proactive issue detection speeds up recovery and reliability. You will collaborate with software engineers and data teams to maintain deployments, observability, and reliability across Azure-based environments and

Qualifications

• Own L2/L3 production support across in-house apps (.NET/C#, Python), APIs, event-driven systems, and data platforms.
• Lead incident triage, diagnosis, and RCA with post-incident reviews.
• Manage incident, problem, and change processes per ITIL best practices.
• Coordinate with development, infra, and vendors for complex production issue resolution.
• Maintain production verification suites, canary checks and reliability playbooks.

Responsibilities

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

Skills

L2/L3 production support

Incident triage

ITIL aligned processes

Azure DevOps

Terraform

AKS / Kubernetes

Docker

CI/CD

AI tools

Dynatrace

ServiceNow

OpenTelemetry

Data observability

Tools

Azure resources management

StoneBranch

Databricks

Palantir Foundry

RabbitMQ / Kafka

Job description

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

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

Why Luxoft?

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 DevOps 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 DevOps 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.

Responsibilities

• Coordinate resolution across platform engineering, Data Engineers and data domain teams (Databricks, Snowflake, Palantir);
• Ensure correct usage of ITSM tools (e.g., ServiceNow) for incident tracking and lifecycle management;
• Monitor critical workflows using Dynatrace;
• Workflows orchestration using StoneBranch;
• 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.

Skills

Production Support and Incident 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.

DevOps and CI/CD

• Own and maintain CI/CD pipelines in Azure DevOps: 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.

Cloud and Infrastructure

• 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.

Observability and Monitoring

• 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 OpenTelemetry: 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.

Application and Data Platform Support

• 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 migrations (SQL Server, MongoDB).
• Scheduling and Orchestration: Manage batch workloads in StoneBranch, Airflow, or equivalent.

AI-First Engineering (How We Work)

• AI-powered incident triage: use AI tools to cluster alerts, suggest root causes, and recommend resolution steps from historical incident data.
• Automated runbooks: build and maintain AI-assisted diagnostic scripts that accelerate common troubleshooting workflows.
• AIOps: leverage AI/ML for anomaly detection, predictive alerting, and capacity forecasting across applications and data pipelines.
• AI coding assistants (GitHub Copilot, Cursor) used daily for scripting, automation, configuration, and documentation.
• Support AI-based applications and services deployed by D&E teams; understand model serving, inference pipelines, and their operational requirements.
• Responsible AI: follow engineering playbooks for data handling (no sensitive data in prompts), security, and observability of AI-generated changes.

Scripting and Automation

• Python scripting for operational automation, data validation, and diagnostic tooling.
• PowerShell for Windows administration and Azure resource management.
• Build self-healing mechanisms and automated remediation where feasible.

Nice-to-Have Skills

• Palantir (Foundry) — DQC setup in workshop, diagnostics of build failures and data pipelines.
• Monte Carlo — data quality alert management and observability.
• Working knowledge of investment management data: portfolio terminology (maturity date, value date), trade lifecycle, market and risk data; familiarity with domain systems (Optima, Cardia).
• Understanding of data lineage, observability and metadata-driven operations concepts.
• Exposure to enterprise-scale data platforms and batch orchestration / data workflow tools.

Luxoft is committed to fostering a diverse and inclusive workplace.

We show fairness to all throughout our talent acquisition and management process.