DataOps Consultant, Lakehouse Support - Abu Dhabi, United Arab Emirates
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Key skills for this role
About the Role
Luxoft is seeking a DataOps Consultant for L2 Lakehouse Support in an investment management environment. The role involves incident management, operational diagnostics, and root cause analysis for batch and streaming data pipelines on Databricks and Delta Lake.
Key Skills for This Role
Responsibilities
- Own end to end L2 incident resolution for Lakehouse data platforms
- Perform operational diagnostics across batch and streaming data pipelines
- Coordinate issue resolution with Platform Engineering, Data Engineering, and business teams
- Manage incidents using ITSM tools such as ServiceNow
- Troubleshoot issues using metadata, data lineage, and technical documentation
- Maintain clear ownership, escalation, and communication during incidents
- Conduct RCA and implement preventive measures through problem management
- Support change management activities and ensure operational stability
Requirements
- Strong experience in Data Operations (L2 Support)
- Hands on experience with Lakehouse architectures (Databricks, Delta Lake, or equivalent)
- Expertise in incident management and ITSM processes
- Experience troubleshooting batch and streaming data pipelines
- Knowledge of metadata driven operations, data lineage, and observability
- Strong skills in Root Cause Analysis (RCA), change management, and problem management
- Experience owning incidents from identification to resolution
- Working knowledge of investment management data: Portfolio & Positions, Trade Lifecycle, Market Data, Risk & Performance Data
- Experience as a Data Engineer in Databricks and Delta Lake
- Hands on knowledge of Delta Lake internals
Full Job Posting
Project Description
- L2 DataOps Consultant – Lakehouse Support for an investment management environment
- Provide operational diagnostics and incident management for Lakehouse data platforms
- Own L2 incident resolution across data pipelines
- Collaborate with Platform Engineering, Data Engineers, and Data Domain teams
- Apply ITSM, change management, and problem management to minimize recurring issues
- Utilize investment management domain knowledge for faster incident resolution and root cause analysis (RCA)
Key Responsibilities
- Own end to end L2 incident resolution for Lakehouse data platforms
- Perform operational diagnostics across batch and streaming data pipelines
- Coordinate issue resolution with Platform Engineering, Data Engineering, and business teams
- Manage incidents using ITSM tools such as ServiceNow
- Troubleshoot issues using metadata, data lineage, and technical documentation
- Maintain clear ownership, escalation, and communication during incidents
- Conduct RCA and implement preventive measures through problem management
- Support change management activities and ensure operational stability
Mandatory Skills
- Strong experience in Data Operations (L2 Support)
- Hands on experience with Lakehouse architectures (Databricks, Delta Lake, or equivalent)
- Expertise in incident management and ITSM processes
- Experience troubleshooting batch and streaming data pipelines
- Knowledge of metadata driven operations, data lineage, and observability
- Strong skills in Root Cause Analysis (RCA), change management, and problem management
- Experience owning incidents from identification to resolution
- Working knowledge of investment management data: Portfolio & Positions, Trade Lifecycle, Market Data, Risk & Performance Data
- Experience as a Data Engineer in Databricks and Delta Lake
- Hands on knowledge of Delta Lake internals
Nice to Have Skills
- Palantir Foundry – DQC setup, pipeline diagnostics, build failure troubleshooting
- Monte Carlo – Data quality monitoring and observability
- Azure DevOps – Repository management and CI/CD
- Experience with Dynatrace or other observability tools
- Experience with enterprise scale data platforms
- Knowledge of batch orchestration and workflow tools
- Familiarity with investment management systems such as Optima and Cardia
- Understanding of portfolio concepts such as Maturity Date and Value Date
- Strong understanding of data lineage, metadata, and observability concepts
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