Senior Data Engineer

Summary

The purpose of this position is to provide end-to-end technical expertise in designing, building, and maintaining data pipelines, models, and integration frameworks that support analytics, reporting, and AI-driven solutions across the organization. The role is responsible for ensuring data accuracy, consistency, and availability across multiple platforms, including dashboards, customer-facing applications, and backend systems. It encompasses active involvement in data

architecture planning, API-based integration, and performance optimization to enable seamless data flow and actionable insights. The Data Engineer will also collaborate closely with cross-functional teams—including application developers, integration engineers, and business analysts—to ensure that data solutions are aligned with project objectives and core business systems, while supporting timely resolution of Incident Requests (IR) and Service Requests (SR) in accordance with service level expectations.

Main Duties & Responsibilities

• 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

_Skills_

• 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
• Hands-on in design, build, and optimize scalable data pipelines to support analytics, reporting, and AI initiatives
• Hands-on in managing end-to-end data engineering workflows, including ingestion, transformation, validation, and orchestration across cloud and on-prem platforms
• Effective in supporting data operations and enhancement activities, ensuring data quality, availability, and performance for business-critical applications
• Good communication and interpersonal skills, with the ability to translate complex data concepts for technical and non-technical audiences