• Plan and align the project with the Androidization strategy • Gather and validate functional and technical requirements • Design the solution architecture and the data model • Automate integration and processing of operational data using Python • Model and automate refined tables incorporating business rules • Implement monitoring and proactive alerts • Publish and validate the tables in the production environment • Document the entire technical and functional platform architecture • Provide training and formalize the technical handover

• Design, build, and optimize Snowflake-centric data architectures to support enterprise analytics, reporting, and operational use cases • Own dbt transformation layers, including model design, testing, documentation, and deployment best practices • Implement scalable data modeling patterns (star schemas, data vault, dimensional models) aligned to business needs • Develop and maintain reliable data pipelines integrating sources such as Salesforce, application databases, and external client data • Ensure data quality through validation, testing, monitoring, and observability frameworks • Optimize Snowflake performance and cost through query tuning, warehouse design, and efficient data modeling • Partner closely with Analytics, BI, Product, and Engineering teams to deliver trusted, analytics-ready datasets • Contribute to architectural standards, code reviews, and best practices across the CDP team • Document data flows, models, and platform decisions to support long-term scalability and knowledge sharing • Ensure data pipelines and models meet PHI / PII / HIPAA compliance requirements • Support secure access patterns, role-based permissions, and data governance controls

• Design, develop, and manage enterprise-scale batch scheduling and data pipeline workflows • Develop and support data pipelines using ETL/ELT tools and scripting • Monitor, troubleshoot, and optimize batch failures and performance issues • Schedule and monitor workloads in AWS / Azure / GCP environments • Integrate Control-M with data platforms such as Snowflake, Redshift, BigQuery, etc. • Collaborate with application, data, and infrastructure teams to ensure seamless scheduling

• Design and implement a scalable data warehouse or data lakehouse to support analytics, reporting, and business KPIs • Develop and maintain reliable batch and/or streaming data pipelines from internal databases and external systems • Collaborate with stakeholders to translate business requirements into efficient data models and schemas • Establish and maintain data modeling standards and best practices • Implement monitoring, data quality controls, and observability for all data workflows • Provide well‑structured datasets to enable self‑service analytics for BI and data teams • Document the data platform, including lineage, definitions, and contracts, to create a shared source of truth for metrics