Managed Services Consultant - Data Engineering

Serve as the primary owner for all managed service engagements across clients, ensuring consistent meeting of SLAs and KPIs. Continuously improve the operating model, including ticket workflows, escalation paths, and monitoring practices. Coordinate incident triaging and resolution raised by client stakeholders. Collaborate with client and internal cluster teams to manage operational roadmaps, recurring issues, and enhancement backlogs. Lead a team of Data Engineers and Consultants, ensuring high-quality delivery and adherence to standards. Support transitions from project mode to Managed Services, including knowledge transfer, documentation, and platform walkthroughs. Ensure up-to-date documentation for architecture, SOPs, and common issues. Contribute to service reviews, retrospectives, and continuous improvement planning, reporting on service metrics, root cause analyses, and team utilization to stakeholders. Participate in resourcing and onboarding planning with engagement managers, resourcing managers, and internal cluster leads. Coach and mentor junior team members, promoting skill development and strong teamwork. Represent JMAN positively, uphold company values, respect others, and honor commitments, including punctuality and timely delivery. Job Requirements Experience in managing support for modern data platforms across Azure, Databricks, Fabric, or Snowflake environments. Strong understanding of data engineering and analytics concepts, including ELT/ETL pipelines, data warehousing, and reporting layers. Hands-on understanding of SQL and scripting languages (Python preferred) for debugging/troubleshooting. Proficient with cloud platforms like Azure and AWS; familiarity with DevOps practices is a plus. Familiarity with orchestration and data pipeline tools such as ADF, Synapse, dbt, Matillion, or Fabric. Understanding of monitoring tools, incident management practices, and alerting systems (e.g., Datadog, Azure Monitor, PagerDuty). Strong stakeholder communication, documentation, and presentation skills. Experience working with global teams and collaborating across time zones. ETL or ELT: Azure Data Factory, Databricks, Synapse, dbt (any two Mandatory). Data Warehousing: Azure SQL Server/Redshift/Big Query/Databricks/Snowflake (Anyone - Mandatory). Data Visualization: Looker, Power BI, Tableau (Basic understanding to support stakeholder queries). Cloud: Azure (Mandatory), AWS or GCP (Good to have). SQL and Scripting: Ability to read/debug SQL and Python scripts. Monitoring: Azure Monitor, Log Analytics, Datadog, or equivalent tools. Ticketing & Workflow Tools: Freshdesk, Jira, ServiceNow, or similar. DevOps: Containerization technologies (e.g., Docker, Kubernetes), Git, CI/CD pipelines (Exposure preferred). About Company JMAN Group is a fast-growing data engineering & data science consultancy that works primarily with Private Equity Funds and their Portfolio Companies to create commercial value using Data & Artificial Intelligence. We also work with growth businesses, large corporates, multinationals, and charities. Our team of over 450 people is a unique blend of individuals with skills across commercial consulting, data science, and software engineering.