Lead Data Engineer

About the Company JMAN Group is a fast-growing data engineering & data science consultancy. We work primarily with Private Equity Funds and their Portfolio Companies to create commercial value using Data & Artificial Intelligence. In addition, we also work with growth businesses, large corporates, multinationals, and charities. We are headquartered in London with Offices in New York, London and Chennai. Our team of over 450 people is a unique blend of individuals with skills across commercial consulting, data science and software engineering. We were founded by cousins Anush Newman (Co-founder & CEO) and Leo Valan (Co-founder & CTO) and have grown rapidly since 2019. In May 2023 we took a minority investment from Baird Capital and in January 2024 we opened an office in New York with the ambition of growing our US business to be as large as, if not bigger than, our European business by 2027. Responsibilities Serve as the primary owner for all managed service engagements across all clients, ensuring SLAs and KPIs are met consistently. Continuously improve the operating model, including ticket workflows, escalation paths, and monitoring practices. Coordinate triaging and resolution of incidents and service requests raised by client stakeholders. Collaborate with client and internal cluster teams to manage operational roadmaps, recurring issues, and enhancement backlogs. Lead a >40 member team of Data Engineers and Consultants across offices, ensuring high-quality delivery and adherence to standards. Support transition from project mode to Managed Services – including knowledge transfer, documentation, and platform walkthroughs. Ensure documentation is up to date for architecture, SOPs, and common issues. Contribute to service reviews, retrospectives, and continuous improvement planning. Report on service metrics, root cause analyses, and team utilization to internal and client stakeholders. Participate in resourcing and onboarding planning in collaboration with engagement managers, resourcing managers and internal cluster leads. Act as a coach and mentor to junior team members, promoting skill development and strong delivery culture. Qualifications 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). Required Skills 7+ years of experience in managing Data & Analytics service delivery, preferably within a Managed Services or consulting environment. Strong understanding of data engineering and analytics concepts, including ELT/ETL pipelines, data warehousing, and reporting layers. Experience in ticketing, issue triaging, SLAs, and capacity planning for BAU operations. 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.