• Drive data architecture unification across multiple SaaS products, creating coherent patterns while respecting product needs. • Partner with vertical technical leads to provide horizontal architectural support and alignment. • Design and optimize data storage across multiple technologies—AWS OpenSearch/Elasticsearch, relational databases, S3, NoSQL, and data warehousing. • Optimize for performance and resilience— indexing, querying, high availability, redundancy, and disaster recovery at scale. • Support AI initiatives—partner with our AI specialist team on data architecture for AI capabilities. • Bring clarity from ambiguity—translate complex challenges into clear architectural direction. • Build capability, not dependencies—mentor engineers so teams become more self-sufficient. • Balance performance, cost, and reliability across our platform.

• Design and implement scalable data ingestion frameworks for EHR and healthcare systems. • Build reusable connectors and integration patterns for diverse clinical platforms. • Architect and maintain flexible, unified data warehouse schemas. • Develop robust ETL/ELT pipelines with high reliability and data quality. • Set up monitoring, logging, and error-handling mechanisms. • Own projects end-to-end, from technical design to delivery. • Select and define the appropriate tech stack for projects.

• Efficient and effective project delivery is the primary responsibility of the tech lead. • Provide leadership and guidance to the data engineering team, including mentoring, coaching, and fostering a collaborative work environment. Set clear goals, assign tasks, and manage resources to ensure successful project delivery. Work closely with developers to support them and improve data engineering processes. • Support team members with troubleshooting and resolving complex technical issues and challenges. • Utilize and promote Generative AI tools to accelerate project delivery. • Provide technical expertise and direction in data engineering, guiding the team in selecting appropriate tools, technologies, and methodologies. Stay updated with the latest advancements in data engineering and ensure the team follows best practices and industry standards. • Collaborate with stakeholders to understand project requirements, define scope, and create project plans. • Support project managers to ensure that projects are executed effectively, meeting timelines, budgets, and quality standards. Monitor progress, identify risks, and implement mitigation strategies. • Act as a trusted advisor for the customer. • Oversee the design and architecture of data solutions, collaborating with data architects and other stakeholders. Ensure data solutions are scalable, efficient, and aligned with business requirements. Provide guidance in areas such as data modeling, database design, and data integration. • Align coding standards, conduct code reviews to ensure proper code quality level. • Identify and introduce quality assurance processes for data pipelines and workflows. • Optimize data processing and storage for performance, efficiency and cost savings. • Evaluate and implement new technologies to improve data engineering processes on various aspects (CICD, Quality Assurance, Coding standards). • Act as main point of contact to other teams/contributors engaged in the project. • Maintain technical documentation of the project, control validity and perform regular reviews of it. • Ensure compliance with security standards and regulations.

• Collaborate with stakeholders to understand business requirements and translate them into data engineering solutions. • Design and oversee the overall data architecture and infrastructure, ensuring scalability, performance, security, maintainability, and adherence to industry best practices. • Define data models and data schemas to meet business needs, considering factors such as data volume, velocity, variety, and veracity. • Select and integrate appropriate data technologies and tools, such as databases, data lakes, data warehouses, and big data frameworks, to support data processing and analysis. • Create scalable and efficient data processing frameworks, including ETL (Extract, Transform, Load) processes, data pipelines, and data integration solutions. • Ensure that data engineering solutions align with the organization's long-term data strategy and goals. • Evaluate and recommend data governance strategies and practices, including data privacy, security, and compliance measures. • Collaborate with data scientists, analysts, and other stakeholders to define data requirements and enable effective data analysis and reporting. • Provide technical guidance and expertise to data engineering teams, promoting best practices and ensuring high-quality deliverables. Support to team throughout the implementation process, answering questions and addressing issues as they arise. • Oversee the implementation of the solution, ensuring that it is implemented according to the design documents and technical specifications. • Stay updated with emerging trends and technologies in data engineering, recommending and implementing innovative solutions as appropriate. • Conduct performance analysis and optimization of data engineering systems, identifying and resolving bottlenecks and inefficiencies. • Ensure data quality and integrity throughout the data engineering processes, implementing appropriate validation and monitoring mechanisms. • Collaborate with cross-functional teams to integrate data engineering solutions with other systems and applications. • Participate in project planning and estimation, providing technical insights and recommendations. • Document data architecture, infrastructure, and design decisions, ensuring clear and up-to-date documentation for implementation, reference and knowledge sharing.