About the role
Own and define end-to-end data and analytics architecture for CoreAI and Foundry platforms, setting long-term technical direction for scalable, reliable, and cost-effective analytics supporting AI workloads. Design, build, and optimize large-scale, robust data pipelines and architectures that support CoreAI's analytics initiatives. Develop and extend scalable ETL/ELT workflows, ensuring reliable ingestion, transformation, and storage of high-volume, high-velocity data from diverse sources. Data Governance & Trust: follow best practices for data quality, lineage, security, and compliance. Collaborate with stakeholders to define trustworthy data sets and implement rigorous data validation protocols, ensuring CoreAI's analytics are both accurate and auditable. Analytics Enablement: Partner with data scientists, analysts, and business leaders to translate business needs into technical solutions. Enable self-service analytics and empower teams by building data models, semantic layers, and tools that streamline access to trusted information. Cross-Functional Collaboration: Work closely with product managers, software engineers, AI researchers, and business stakeholders to align data solutions with business goals. Translate ambitious vision into actionable roadmaps and measurable outcomes. Mentor senior engineers and technical leads, guide complex design decisions, and foster a culture of engineering excellence, operational rigor, and continuous learning across the organization. Master's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 4+ years experience in business analytics, data science, software development, data modeling, or data engineering OR Bachelor's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 6+ years experience in business analytics, data science, software development, data modeling, or data engineering OR equivalent experience. These
requirements include, but are not limited to the following specialized security screenings: Deep experience architecting and operating large-scale distributed data systems in cloud environments, preferably Azure. Ability to write clean, working code using core algorithms, data structures, and analytics‑oriented problem‑solving. Extensive experience in implementing and optimizing data pipelines and distributed data systems (e.g., Azure Data Lake, Spark, Synapse, SQL databases). Understanding of data governance, privacy, lineage, and security best practices, especially within highly regulated or enterprise environments. Excellent communication skills to convey complex technical concepts to both technical and non-technical audiences. Expertise with modern data engineering languages and frameworks (e.g., Azure Data Explorer, PySpark, SQL, etc). Experience collaborating in cross-functional teams and building consensus among stakeholders with diverse backgrounds (engineering, analytics, product, business). Track record of driving innovation in data engineering, including experience leading technical projects and mentoring others.
