• Collaborate closely with stakeholders, cross‑functional and internal technical teams to gather requirements, document business rules and develop thorough understanding of the business context and objectives • Collaborate to design secure, scalable, and cost‑optimized data solutions • Design, build, and maintain scalable, reliable ETL/ELT data pipelines using AWS, Snowflake and Snowflake tools • Develop and optimize data models, both conceptual and physical, to support analytics, reporting, dashboards and operational consumption • Implement data quality, validation, and monitoring frameworks to ensure accuracy and reliability • Ensure data workflows are modular, testable, and properly version‑controlled • Operationalize pipelines with monitoring, alerting, and automated recovery mechanisms • Conduct advanced data analysis using languages such as Python and SQL • Develop documentation to include data models, data dictionaries, and data usage guides • Improve end-to-end performance of data workflows • Build and maintain CI/CD pipelines using Jenkins to support automated testing, deployments, and continuous integration • Meet schedule deadlines and commitments with a high-level of quality of deliverables • Collaborate with a team of cross-functional resources in an Agile delivery environment to deliver iterative value

• Lead the design, development, and implementation of scalable data pipelines and infrastructure using Azure Databricks, Azure Data Factory, SQL Server, Salesforce integrations, and related technologies • Develop and maintain both OLTP and dimensional data models based on business requirements, applying Kimball and Inmon methodologies as appropriate • Optimize data storage and retrieval performance across SQL Server and cloud data platforms • Integrate data from Salesforce and other SaaS applications via APIs and extraction workflows • Implement and maintain batch and real-time data processing architectures • Participate in code and architecture reviews to ensure adherence to data engineering standards and best practices • Analyze and troubleshoot data pipeline and performance issues, providing timely and effective solutions • Contribute to the continuous improvement of data engineering practices, tools, and processes • Stay current with emerging trends in cloud data platforms and identify opportunities for innovation • Champion comprehensive documentation of data architecture, lineage, and modeling decisions • Facilitate knowledge sharing and mentorship across the team; engage cross-functionally to align on architectural vision • Drive adoption of best practices including data governance, quality, security, and compliance standards • Lead initiatives to reduce technical debt and improve pipeline performance and cost efficiency.

• Execute development engineering design activities, system performance modeling, and related CAD design activities by coordinating with discipline-specific (electrical, civil, structural) design teams • Review and oversee the delivery of preliminary designs (0–30%) and feasibility studies • Maintain professional understanding of all applicable codes, standards, and construction practices • Act as a liaison between design teams and relevant internal and external stakeholders • Work cross-functionally across departments to identify and align team members across disciplines to ensure project work is completed and submitted in a timely manner • Maintain strong and responsive company-client relationships, particularly for communication related to specific assignments and meeting topics • Ensure adherence to company and client QA/QC standards • Assist in the development, implementation, and enforcement of departmental Standard Best Practices (SBPs) • Support the development, maintenance, and improvement of development-phase performance analysis tools • Work with proposal teams and subject matter experts (SMEs) to establish proposal pricing • Provide engineering and technical support to members of the Business Development & Project Services teams • Provide pre-sales technical support to customers and NEI outside sales teams • Support business development through Seller-Doer activities, including marketing support and proposal preparation as needed • Attend conferences, training workshops, seminars, and continuing education activities as appropriate to maintain quality of work and support professional advancement • Maintain high ethical and professional standards • Attend required safety training and adhere to all safety rules and guidelines • Perform other responsibilities as assigned

• Partner directly with enterprise customers and business stakeholders to understand data challenges, define requirements, and deliver scalable solutions. • Own technical delivery for customer engagements from discovery and solution design through implementation and adoption. • Help customers maximize the value of Resilinc’s data and analytics capabilities through effective solution design and execution. • Present technical architectures, implementation plans, and business outcomes to both technical and executive audiences. • Design, build, and maintain scalable batch and real-time data pipelines and data products using Databricks, ClickHouse, Python, and distributed data processing frameworks. • Architect modern cloud-native data platforms leveraging Databricks, Delta Lake, Azure Data Services, and distributed processing technologies. • Develop robust ETL/ELT frameworks, data models, and data quality monitoring processes. • Optimize data storage, query performance, scalability, reliability, and cost efficiency across large-scale datasets. • Build and maintain data products that enable analytics, reporting, operational workflows, and AI/ML applications. • Collaborate closely with Product, Engineering, Solutions Consulting, and Customer Success teams to deliver customer outcomes. • Drive best practices in data governance, security, observability, reliability, and operational excellence. • Contribute to architecture decisions, technical design reviews, and platform evolution. • Mentor junior engineers and contribute to engineering best practices.