• Design, build, and operate core platform systems, including schema management, streaming frameworks, and event mesh systems. • Define technical standards and patterns for the Streaming Data Platform team. • Lead cross-team design reviews for systems that interact with or depend on the streaming platform. • Drive platform reliability, performance, and cost strategy across streaming services. • Design and implement shared libraries, frameworks, and tooling for stream consumers. • Partner with product and engineering teams to translate requirements into platform capabilities. • Produce technical proposals and documentation that communicate design, trade-offs, and long-term implications. • Serve as an escalation point for complex streaming issues and high-impact incidents. • Sponsor and grow senior engineers through technical mentorship and challenging assignments.

• Analyze legacy databases from various school ERPs, understanding their tables, relationships and implicit rules. • Investigate, map and interpret raw data, identifying inconsistencies, patterns and exceptions. • Create, refine and execute complex SQL scripts, including advanced SELECTs, JOINs, aggregations, bulk updates and data cleaning processes. • Transform diverse datasets to the format required by Activesoft, ensuring data integrity, completeness and quality. • Identify issues such as missing data, corrupted structures, improperly used fields and divergent rules, proposing practical solutions. • Document findings, recurrent patterns and recommended improvements to the migration process. • Collaborate with implementers and the onboarding team to align business rules and validate final data.

• Design and implement end-to-end data pipelines (ETL/ELT) that ingest, process, and curate large-scale enterprise data, including telemetry/vehicle data and other structured/unstructured sources. • Migrate and modernize data assets to a centralized data platform (e.g., BigQuery) using principled data lake/warehouse architectures (Bronze/Silver/Gold or Medallion architecture) to power analytics and reporting. • Architect scalable data models and data warehouses, optimizing for query performance, maintainability, and cost efficiency. • Develop and operate robust orchestration pipelines using Airflow/Astronomer or Schedule Query, with secure, reproducible CI/CD workflows (Terraform + Git). • Build and maintain reliable data quality checks, lineage, and monitoring with observability tools (e.g., Splunk, Looker/Grafana/Tableau/Power BI dashboards) to rapidly detect and resolve data issues. • Implement data governance, security, and compliance controls (data lineage, access controls, PII/PHI protection) in collaboration with security and privacy teams. • Lead the design and delivery of analytics-ready data assets for cross-functional teams, including dashboards, alerts, and self-service analytics. • Mentor and coach junior engineers, review code, and drive best practices in data engineering, testing, and documentation. • Collaborate with data scientists, product managers, and business stakeholders to translate requirements into scalable data solutions and timely insights. • Monitor cost and capacity planning for cloud resources; optimize storage and compute usage across GCP services (BigQuery, Dataflow, Dataproc, GCS). • Participate in on-call rotations and incident response to maintain high availability of data services.

• Develop, maintain, and optimize data processing systems and ETL pipelines using tools such as Azure Data Factory and Azure Databricks. • Collaborate with the Data Architect to implement data models and architectures that support scalable and reliable data flows. • Ensure data quality and integrity through unit tests with Pytest and component and integration tests with Behave. • Work with cross-functional teams to integrate data from various sources, utilizing PySpark processes executed in Databricks and orchestrated by Azure Data Factory. • Manage infrastructure components in the Azure Cloud, including role-based access control and Infrastructure as Code (IaC) practices. • Contribute to continuous integration and continuous deployment (CI/CD) processes using Azure DevOps and related tools. • Perform data analysis and processing using SQL, Python, and Spark to support business intelligence and reporting needs.