Leads the design, development, deployment, and maintenance of data and analytics platforms. Develops reliable, scalable, and efficient data pipelines and data processing solutions that enable data to be effectively processed, stored, governed, and made available to analysts and other data consumers. Collaborates with business stakeholders, IT experts, data scientists, architects, and subject-matter experts to deliver enterprise data and analytics solutions aligned with business and technical requirements.
Key Responsibilities
• Design, develop, and automate distributed data ingestion and transformation solutions using data from relational, event-based, semi-structured, and unstructured sources.
• Build reliable, scalable, and efficient ETL/ELT data pipelines using appropriate tools, technologies, and scripting languages.
• Design and implement data quality, validation, monitoring, and alerting frameworks to identify and resolve data integrity issues.
• Implement data governance practices covering metadata, data access, retention, compliance, and security.
• Design and implement physical data models, including database structures, indexing, and table relationships, to support performance and scalability.
• Develop and operate large-scale data storage and processing solutions across cloud and distributed data platforms, including data lakes, warehouses, and lakehouse environments.
• Optimize data pipelines, Spark workloads, databases, and cloud infrastructure for performance, reliability, scalability, and cost efficiency.
• Integrate data from a variety of enterprise applications and source systems and support real-time and event-driven data processing.
• Develop automation for common and repeatable data preparation, integration, deployment, and platform-management activities to minimize manual and error-prone processes.
• Implement CI/CD and DevOps practices to support automated deployment, testing, and release management.
• Participate in troubleshooting, testing, validation, and continuous improvement of data pipelines and platform solutions.
• Ensure data platforms and solutions meet applicable quality, governance, security, compliance, and regulatory requirements.
• Collaborate with data scientists, analysts, architects, IT teams, and business stakeholders to understand requirements and deliver effective data solutions.
• Document data solutions, processes, designs, and technical information to support knowledge transfer and operational effectiveness.
• Apply Agile development methodologies such as Scrum and Kanban to deliver data engineering initiatives.
• Provide technical leadership and mentor less experienced team members, promoting engineering excellence and collaboration.
Originally posted on Himalayas