Data Quality Monitoring Lead

Position : Data Quality Monitoring Lead Location : Hyderabad, India (Work from Office) Position Overview : The Data Quality Monitoring Lead is a pivotal role responsible for ensuring the integrity, accuracy, and reliability of data across various systems and platforms. This position involves leading a dedicated offshore team, implementing robust data quality monitoring frameworks, and collaborating with cross-functional stakeholders to address data-related challenges proactively. Key Responsibilities : 1. Incident Response & Monitoring : - Continuous Monitoring : Oversee the real-time monitoring of data pipelines, dashboards, and logs using tools like Log Analytics, KQL queries, and Azure Monitoring to detect anomalies and data quality issues promptly - Alert Management : Configure and fine-tune alerting mechanisms to ensure timely notifications of potential data discrepancies, enabling swift response and mitigation.? - Incident Coordination : Collaborate with support teams to investigate and resolve system-related issues impacting data quality, ensuring minimal disruption to business operations.? - Documentation & Escalation : Maintain comprehensive records of incidents, including root cause analysis, corrective actions taken, and lessons learned. Escalate unresolved issues to appropriate stakeholders for further investigation 2. Triaging & Issue Analysis : - Data Quality Assessment : Lead the team in identifying and categorizing data quality issues originating from system integrations such as Salesforce, Nexsure, and Mulesoft - Root Cause Analysis (RCA) : Utilize analytical tools and techniques to perform in-depth investigations into data discrepancies, determining underlying causes and contributing factors. - Stakeholder Collaboration : Engage with system support teams, business users, and data stewards to validate findings, discuss potential solutions, and implement corrective measures.? 3. Issue Resolution & Data Analysis : - Corrective Actions : Develop and implement strategies to rectify identified data quality issues, including data cleansing, transformation, and enrichment processes. - Proactive Monitoring Tools : Design and enhance monitoring tools, scripts (using Python, SQL), and dashboards (e.g., Power BI) to facilitate early detection of data anomalies and streamline issue resolution workflows - Cross-System Analysis : Conduct comprehensive data analysis across multiple systems (e.g., Salesforce, Nexsure) to identify synchronization issues, data inconsistencies, and potential bottlenecks.? 4. Process & Monitoring Improvements : - Process Optimization : Evaluate existing data monitoring processes and workflows, identifying areas for improvement to enhance efficiency and effectiveness - Tool Enhancement : Collaborate with the broader Data Quality team to refine and upgrade monitoring tools, dashboards, and standard operating procedures (SOPs) to align with evolving business needs and technological advancements - Best Practices Implementation : Promote the adoption of industry best practices in data quality monitoring, ensuring consistency and standardization across all data-related activities. 5. Team Leadership & Knowledge Management : Team Management : - Lead and mentor a team of offshore DataOps professionals, providing guidance, training, and support to foster a collaborative and high-performing work environment. Knowledge Sharing : - Develop and maintain a centralized knowledge base, including SOPs, troubleshooting guides, and best practices, to facilitate information sharing and continuous learning within the team Stakeholder Communication : - Serve as the primary liaison between the offshore team and the Lockton Data Quality Lead, providing regular updates, reporting on key performance indicators (KPIs), and escalating issues as necessary Required Skills & Qualifications : Technical Skills : - Data Monitoring & Analytics : Proficiency in using Log Analytics, KQL, Azure Monitoring, and Power BI for data visualization and analysis - Database & Querying : Strong command of SQL, particularly with Azure SQL and Synapse, for data extraction, manipulation, and analysis - Automation & Scripting : Experience in developing automation scripts using Python to streamline data validation and monitoring processes - Azure Suite : Familiarity with Azure services such as Logic Apps, ADLS, ADF, and Synapse; engineering-level proficiency is preferred - Integration Knowledge : Understanding of data flows and integration patterns involving Mulesoft and Salesforce platforms - DevOps Tools : Experience with Azure DevOps (ADO) for issue tracking, version control, and collaborative development data standards and regulatory requirements. (ref:hirist.tech)

Location: hyderabad, IN

Posted Date: 5/9/2025