• Architect & Build Data Pipelines: Design, construct, install, test, and maintain highly scalable data management systems and ETL/ELT pipelines. • Integrate Diverse Data Sources: Develop processes to ingest and integrate high-volume, high-velocity data from SCADA systems, historians, DCS, PLC, and IoT sensors. • Cloud Data Platform Development: Implement and manage data solutions on Microsoft Azure, leveraging services like Azure IoT Hub. • Data Modelling & Warehousing: Design and implement data models optimized for time-series data from industrial assets. • Enable Advanced AI: Build the data infrastructure to support AI/ML models for predictive maintenance and anomaly detection. • Champion Master Data Management (MDM): Create a single source of truth for critical data domains. • Ensure Data Quality & Governance: Implement data quality checks and monitoring to ensure accuracy and consistency. • Embrace Industry Standards: Implement industry-specific standards for interoperability across sectors. • Collaborate & Innovate: Work with cross-functional teams to understand data needs and deliver effective solutions. • Automate & Optimize: Identify automation opportunities to improve data delivery and scalability. • Security First: Maintain security best practices to protect sensitive data assets.

• Lead the design, development, and enhancement of ETL pipelines and backend data workflows using Python and SQL • Design and optimize large-scale data processing solutions on AWS using services such as Glue, EMR, Lambda, and S3 • Lead the implementation and improvement of CI/CD pipelines to automate build, test, and deployment for data and application code • Collaborate with architects, product owners, and business partners to refine requirements and translate them into scalable, secure data solutions that follow approved architecture patterns • Write high quality, maintainable, and well-tested code, and set a strong example for coding and design practices • Investigate and resolve complex technical issues, data quality problems, and performance bottlenecks across environments • Participate in and often lead code reviews, helping to maintain high standards for code quality, testing, and documentation • Mentor junior and mid-level engineers through pairing, feedback, and knowledge sharing • Contribute to the evolution of data and application architecture, development processes, and engineering best practices • Collaborate with cross-functional teams to debug, improve, and support data products and services in production • Continuously learn and adopt modern technologies, tools, and development practices relevant to data engineering and cloud platforms.

• Analyze and develop solutions to information needs in our business • Design, develop, deploy, and test new information and data processing applications in accordance to our quality standards • Perform preventive maintenance, identify and execute improvements, and refactor existing code base, including our legacy applications • Monitor, respond, and resolve incidents in production • Develop and maintain the infrastructure that host our applications in production • Support our internal staff and stakeholders’ inquiries and issues with the applications in use • Investigate and create proofs of concepts, such as in using external services, APIs, programming libraries, and data source systems • Contribute in the development of user guides and knowledge base of applications in use

• designing, building, and maintaining dimensional data models that support our analytics, AI, and business intelligence initiatives • working closely with stakeholders to ensure data is accessible, reliable, and optimized for performance • creating and maintaining logical, physical, and conceptual data models in Google Big Query using Dataform • collaborating with stakeholders to understand business needs and deliver data products in support of those needs • developing and managing data dictionaries, metadata, and data lineage • refining and updating models for performance, scalability, and cost efficiencies • ensuring data quality, integrity, and security across all data platforms