• Develop, construct, and maintain large-scale data processing systems that collect data from a variety of structured and unstructured sources — using AI code generation tools to accelerate pipeline authoring, reduce boilerplate, and improve code quality. • Build and optimize ELT pipelines using AI-assisted tooling to identify bottlenecks, suggest optimizations, and automate routine pipeline maintenance tasks. • Identify, design, and implement internal process improvements: use AI to automate manual processes, optimize data delivery, and re-design infrastructure for greater scalability — replacing manual analysis with AI-driven discovery of improvement opportunities. • Build the infrastructure required for optimal extraction, transformation, and loading of data from various sources; use AI to accelerate infrastructure-as-code authoring and configuration. • Prepare data for data scientist exploration and discovery using AI-assisted data profiling and quality assessment tools — surfacing anomalies, schema drift, and data gaps faster than manual inspection allows. • Perform data wrangling and munging for downstream analytics and machine learning; leverage AI tools to generate and validate transformation logic against business rules. • Assemble large, complex datasets that meet functional and non-functional business requirements; use AI to rapidly evaluate dimensional modeling approaches and ontology alignment strategies. • Enable large-scale machine learning by designing and maintaining annotated datasets, elastic search approaches, and scalable data lake structures that support AI/ML workloads. • Create and maintain analytics pipelines that generate data and insight to power business decision-making; use AI-assisted analysis to proactively surface trends, anomalies, and opportunities within pipeline outputs. • Collaborate with data scientists, analysts, and business stakeholders on requirements for dimensional modeling, distributed ETL pipelines, and cross-repository data migration. • Evaluate, compare, and improve design patterns, data lifecycle approaches, and data ontology alignment — using AI to model trade-offs and accelerate proof-of-concept validation. • Work with data and analytics experts to continuously improve the functionality, reliability, and intelligence of data systems. • Perform root cause analysis on internal and external data and processes using AI-assisted investigation tools — replacing slow, manual log and lineage review with faster, AI-accelerated diagnostics. • Develop and maintain data quality frameworks; use AI to automate anomaly detection, schema validation, and data contract enforcement across pipelines. • Develop a strong understanding of company domains, strategic direction, and user needs to ensure data systems are aligned to business outcomes, not just technical requirements.

• Define and maintain enterprise data architecture standards across structured, semi-structured, and unstructured data domains. • Use AI-assisted modeling tools to accelerate data model design and validate designs against business requirements. • Design and govern the organization's cloud data lake, data warehouse, and lakehouse architectures on AWS. • Establish data ontology, taxonomy, and semantic layer standards that enable AI systems to reason over organizational data accurately. • Evaluate emerging data architecture patterns and build a roadmap for their adoption across Stellus Rx. • Design scalable data models and ELT/ETL pipeline architectures that support analytics and AI/ML workloads. • Define standards for data partitioning, indexing, caching, and storage optimization. • Partner with Data Engineers to translate architectural blueprints into production-ready pipelines. • Define and enforce data governance frameworks, data quality standards, and data contracts across the enterprise. • Develop and maintain a master data management (MDM) strategy.

• Design, develop, and maintain scalable data pipelines for ingestion, transformation, and delivery of large datasets across diverse industries. • Implement and ensure data privacy and security best practices, supporting data sovereignty and compliance with regulatory requirements. • Collaborate closely with AI/ML engineers, Data Scientists, and Platform engineers to enable advanced analytics and AI capabilities while retaining strict data control. • Optimize data platforms and systems for performance, reliability, and cost efficiency. • Build tools and frameworks for secure, privacy-preserving data processing and orchestration. • Develop and maintain documentation, data models, and technical workflows. • Partner with cross-functional teams to launch new data-driven product features and solutions.

• Utilize consulting and technical skills to be able to work in a client-facing project environment independently. • Be responsible for your own execution and sometimes lead individual work streams on client engagements as assigned and under supervision of engagement lead. • Collaborate with other team members to successfully deliver on projects. • Work effectively and directly communicate with both internal and client and/or partner teams. • Develop full ownership of your execution on client engagements, you'll become involved in the project planning and solution stages of engagements as well. • Design and implement complex ETL/ELT pipelines with evidence of improved data processing times. • Successfully lead small data warehousing projects with measurable performance enhancements under the management of an engagement lead. • Contribute to real-time data processing solutions and managed streaming data. • Implement security and compliance measures for data pipelines. • Design and implement version control and branching strategies and integrate them into CI/CD for promoting and testing in higher environments.