• Design, develop, and maintain Azure Data Factory pipelines, including orchestration, parameterization, and trigger management. • Configure and manage linked services and datasets within ADF. • Monitor, troubleshoot, and optimize ADF pipelines to ensure performance and reliability. • Develop and maintain ETL/ELT processes and support the evolution of the data warehouse layer (Azure SQL Database or Synapse). • Translate business requirements into technical solutions in close collaboration with the Product Owner and Architect. • Develop and maintain Python-based components (e.g., Azure Functions, API integrations, automation scripts). • Contribute to CI/CD processes in Azure DevOps, including pipelines, releases, and environment promotion. • Support occasional Power BI reporting and dashboarding needs. • Ensure proactive communication, stakeholder alignment, and visibility of risks and impacts. • Take ownership of assigned tasks and actively contribute to continuous platform improvement.

• Design and own Salesforce data models across core clouds (Sales, Service, Financial Services, Consumer Goods) • Define object models, relationships, field strategy, and data lifecycle management • Ensure scalability, performance, and alignment with Salesforce best practices • Lead data governance, naming conventions, and standards • Architect and oversee data migrations from legacy systems (ERP, CRM, custom databases) • Define strategies for data synchronization, deduplication, and master data management • Establish and enforce data quality frameworks (validation, deduplication, enrichment) • Partner with stakeholders on data ownership, stewardship, and compliance • Support regulatory and security requirements (PII, GDPR, role-based access) • Design data structures optimized for Salesforce reporting, dashboards, and analytics • Enable downstream use cases for Einstein, AI, and advanced analytics • Support Data Cloud and Customer 360 initiatives where applicable

• Designing, developing, and maintaining data pipelines and services with a focus on simplicity, scalability, reliability, and performance. • Writing high-quality, well-tested code using Python and SQL. • Building and maintaining data pipelines (ETL/ELT) using tools like Airflow. • Implementing comprehensive automated testing to ensure the quality and reliability of our data products. • Collaborating with cross-functional teams to deliver impactful features. • Participating in the design and architecture of our evolving data platform. • Troubleshooting and resolving bugs and issues in a timely manner. • Championing best practices for code and data quality and testing. • Driving improvements in our development processes using Lean and Agile principles. • Managing and scaling data products, ensuring they are meeting the evolving needs of the business.

Position Overview V4C.ai is seeking a motivated Data Engineer to join our remote team in the United States. In this role, you will support the design, development, and maintenance of data solutions using Databricks, helping clients and internal teams process, transform, and analyze data effectively. You'll work on building reliable data pipelines and workflows in a collaborative environment, gaining hands-on experience with modern data engineering tools and cloud technologies. Key Responsibilities - Collaborate with team members and stakeholders to understand data requirements and contribute to building scalable data pipelines and workflows in Databricks. - Develop and implement ETL/ELT processes using Databricks, Python, SQL, and related tools to ingest, transform, and prepare data. - Assist in optimizing data workflows for better performance, reliability, and cost-efficiency within Databricks environments. - Support the creation and maintenance of data models, tables, and integrations in cloud platforms (Azure, AWS, or similar). - Work closely with cross-functional teams (data analysts, scientists, and engineers) to deliver clean, accessible data for analytics and reporting. - Monitor data pipelines, troubleshoot basic issues, and contribute to documentation and best practices. - Stay curious about new Databricks features and data engineering trends to support ongoing improvements. Requirements - Bachelor's degree in Computer Science, Data Science, Engineering, Information Systems, or a related field (or equivalent practical experience). - 1-2 years of professional experience in data engineering, data processing, analytics engineering, or a closely related role (internships, co-ops, or academic projects with relevant tools count toward this). - Hands-on experience and comfort building basic data pipelines or transformations. - Proficiency in Python and SQL; experience with Scala is a plus but not required. - Basic understanding of cloud platforms such as Azure, AWS, or GCP (ex: working with storage, compute, or data services). - Solid analytical and problem-solving skills with attention to detail and a focus on writing clean, maintainable code. - Strong communication skills and ability to work collaboratively in a remote team environment. - Eagerness to learn, take ownership of tasks, and grow within data engineering.