• 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.

• Design, build, maintain, and operationalize data pipelines for high volume and complex data using appropriate tools and practices in development, test, and production environments. • Collaborate within an agile, multi-disciplinary team to deliver optimal data integration and transformation solutions • AAnalyze data requirements (functional and non-functional) to develop and design robust, scalable automated, fault-tolerant data pipeline solutions for business and technology initiatives • Profile data to assess the accuracy and completeness of data sources and provide feedback in data gathering sessions • Develop and design data mappings, programs, routines, and SQL to acquire data from legacy, web, cloud, and purchased package environments into the analytics environment • Understand and apply the appropriate use of ELT, ETL, data virtualization, and other methods to optimize the balance of minimal data movement against performance, and mentor others on their appropriate use • Drive automation of data pipeline preparation and integration tasks to minimize manual and error-prone processes and improve productivity using modern data preparation, integration, and AI-enabled metadata management tools and techniques • Leverage auditing facilities that will enable monitoring of data quality to detect emerging issues. • Deploy transformation rules to cleanse against defined rules and standards • Participate in architecture, governance, and design reviews, identifying opportunities and making recommendations • Participate in health check assessments of the existing environment and evaluations of emerging technologies • Collaborate with architects to design and model application data structures, storage, and integration in accordance with enterprise-wide architecture standards across legacy, web, cloud, and purchased package environments