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Designs and owns scalable analytics data models, pipelines, semantic layers, and data quality practices. Partners with engineering, product, AI, and business stakeholders to translate ambiguous needs into reliable data solutions. Responsibilities include dimensional modeling, transformation workflows, observability, documentation, governance, lineage, warehouse optimization, and self-service analytics enablement using modern cloud data platforms.

Design, build, deploy, and operate reliable software, APIs, services, data pipelines, and cloud applications. Apply AI and machine learning to business workflows, develop prototypes, evaluate technical approaches, and measure software and model performance. Lead complex technical projects, make architecture tradeoffs, maintain production systems, improve reliability, and mentor engineers while collaborating with data scientists and business stakeholders.

Drive DevOps and SRE initiatives across cloud-native data infrastructure. Deploy and manage Kubernetes workloads on AWS and GCP, provision infrastructure with Terraform, maintain CI/CD pipelines, develop Python automation, improve observability and reliability, optimize cloud costs, enforce security standards, and influence cross-functional data and engineering teams.

Build and own production data pipelines, dbt models, semantic layers, and supporting infrastructure across Snowflake, BigQuery, Airflow, Fivetran, Databricks, AWS, and GCP. Partner with stakeholders across business functions to scope requirements, resolve data quality issues, monitor SLAs, support incident response, and apply data governance practices. The role requires autonomous delivery, strong communication, production-grade Python and SQL, and contributions to engineering standards, CI/CD, testing, and documentation.

The role involves building and optimizing big data pipelines, designing data architectures, collaborating with data scientists, and driving technical strategy for data processing infrastructure.