• Design, develop, and maintain scalable data ingestion, transformation, and publishing pipelines utilizing Databricks and AWS services. • Implement and optimize Databricks Lakehouse capabilities including Unity Catalog, Delta Live Tables, Auto Loader, Databricks SQL, and Delta Sharing. • Build and maintain governed data products supporting operational, analytical, reporting, and machine learning workloads. • Develop and support medallion architecture data pipelines and enterprise data quality frameworks. • Implement data governance controls, metadata management, lineage tracking, and data retention policies. • Collaborate with cloud engineers, architects, cybersecurity specialists, and business stakeholders to deliver secure, production-ready solutions. • Optimize platform performance through partitioning, clustering, caching, workload tuning, and query optimization techniques. • Support analytics enablement through semantic layers, dashboards, reporting solutions, and self-service data access capabilities. • Participate in architecture reviews, operational readiness activities, platform modernization initiatives, and continuous improvement efforts. • Create and maintain technical documentation, design artifacts, operational procedures, and engineering standards.

• Data Ingestion & ETL: Build Python/SQL pipelines to ingest invoices, orders, and catalogs. • Perform historical backfills via SFTP/API and manage Airflow DAGs. • Instance Configuration: Set up custom fields, product filtering logic, and sales workflows. • Manage SSO and user provisioning for new rollouts. • AI-Augmented Engineering: Leverage AI coding assistants (Copilot, Cursor) and LLMs to accelerate Python/SQL script generation, data mapping, and debugging. • Customer Communication & Projects: Act as a technical point of contact. Translate complex data issues into clear updates for customers. Own project milestones from kickoff to "Go Live." • Integration & Automation: Build Workato recipes and connect customer ERPs via APIs/webhooks to ensure real-time data flow. • QA & Troubleshooting: Triage HubSpot support tickets, debug data discrepancies in large data sets, and deploy production fixes. • Documentation: Maintain customer data mappings and internal technical runbooks.

• Architect and optimize large-scale data platforms on Google Cloud, with BigQuery as the analytical backbone • Design and build unified batch and streaming pipelines that handle high-volume, mission-critical workloads • Lead infrastructure-as-code practices, ensuring environments are repeatable, secure, and version-controlled • Implement open table formats to enable cross-cloud and cross-engine data interoperability • Establish automated data quality, metadata, and lineage practices across the data estate • Partner with data scientists, analysts, and product teams to translate business needs into reliable data products • Mentor engineers, review designs, and raise the bar on engineering standards

• This role supports the large-scale modernization of legacy systems by leading the migration of over 10TB of documentation and records into a cloud-based environment. • The position focuses on transforming document-heavy workflows into structured, digital processes, while enabling seamless data integration across systems using MuleSoft APIs. • The work directly supports enhanced analytics, reporting, and operational efficiency for a mission-critical enterprise platform. • Lead end-to-end data migration from legacy systems to cloud platforms, including data mapping, transformation, and validation (Salesforce-centric). • Analyze and convert unstructured, document-heavy data into structured, analytics-ready formats using Document AI. • Implement data quality processes (cleansing, deduplication, reconciliation) to ensure accuracy and completeness post-migration. • Support phased migration and system cutover with minimal operational disruption. • Design, build, and maintain MuleSoft integrations, including API development for real-time and batch data exchange. • Apply data transformation logic (e.g., DataWeave) and troubleshoot integration/data flow issues across systems. • Contribute to Salesforce Data 360 implementation, including data harmonization, identity resolution, and unified profiles. • Ensure proper data ingestion, lineage, metadata management, and traceability across the ecosystem. • Ensure compliance with data governance, privacy, and security requirements (e.g., PII). • Collaborate in Agile teams; support testing, documentation, and audit readiness across migration and integration efforts.