Data and AI Ops Engineer

Key Accountabilities ·    Design, develop, and maintain scalable data pipelines and integration workflows to support analytics, reporting, and operational systems ·    Ensure data quality, consistency, and reliability across multiple platforms by implementing robust validation, cleansing, and transformation processes ·    Collaborate with application developers, business analysts, and system owners to define data requirements and deliver solutions aligned with business objectives ·    Optimize data storage and retrieval performance across databases, data lakes, and cloud platforms to support real-time and batch processing needs ·    Support the deployment and enhancement of data models, APIs, and ETL/ELT frameworks, ensuring alignment with architectural standards and governance policies ·    Monitor and resolve data-related Incident Requests (IRs) and Service Requests (SRs) in accordance with service level agreements and operational expectations ·    Participate in planning and execution of data-centric IT projects and initiatives, including resource coordination, risk mitigation, and stakeholder engagement ·    Maintain comprehensive documentation of data flows, schemas, and integration logic to support audit, compliance, and knowledge sharing ·    Collaborate with cross-functional teams to ensure seamless integration of data solutions with core business systems and external platforms ·    Stay abreast of emerging data technologies, tools, and best practices to continuously improve engineering efficiency and solution effectiveness Education/Work Experience ·    Bachelor’s degree in Computer Science, Computer Engineering, Information Systems, Data Analytics or a related field, with at least 6 years of hands-on experience in data engineering, data integration, or analytics platform development, including a minimum of 3 years in designing and maintaining enterprise-grade data pipelines and solutions; OR ·    Diploma in a relevant discipline, with a minimum of 8 years of practical experience in data engineering or related domains, including at least 4 years in a technical lead or senior engineering capacity, supporting cross-functional data initiatives and system integration efforts  ·        Understanding of data architecture principles, including data modeling, pipeline design, ETL/ELT frameworks, and distributed data processing ·        Has knowledge in enterprise data platforms and tools, at least in RDBMS. Knowledge in Azure Data Factory, Databricks, Power BI, and other modern analytics ecosystems is a plus. ·        Familiarity with API-based integration and data exchange protocols (e.g., REST, SOAP, MQ), enabling seamless connectivity between systems and platforms ·        Knowledge of cloud data services and infrastructure (e.g., Azure, AWS, GCP). ·        Understanding of data governance, privacy, and compliance standards, including data lineage, access control, and audit readiness ·        Awareness of AI and machine learning fundamentals, particularly in the context of chatbot development, predictive analytics, and model deployment ·        Experience in business intelligence and reporting frameworks, enabling effective visualization, role-based access, and decision support ·        Up-to-date perspective on emerging data technologies, trends, and best practices relevant to enterprise analytics and AI-driven solutions