• Design, develop, and maintain ETL/ELT workflows using Oracle Data Integrator (ODI) • Create, optimize, and troubleshoot PL/SQL packages, procedures, functions, triggers, and complex SQL queries • Develop and support data integration, data migration, and data transformation processes • Perform performance tuning of ODI interfaces, mappings, and Oracle database objects • Analyze business requirements and translate them into technical specifications and ETL designs • Monitor and resolve production issues, ensuring data accuracy and timely delivery • Implement data quality checks, validation rules, and error-handling mechanisms • Collaborate with business analysts, data architects, and stakeholders during project delivery • Participate in code reviews, testing, deployment, and documentation activities • Ensure adherence to data governance, security, and development standards

• Own our image and video dataset lifecycle: taxonomy, curation, collection workflows, and quality, so our models train on clean, well-structured, error-free data. • Design and maintain the data pipelines that move, transform, version, and sync our datasets (e.g. DVC, annotation platforms, cloud storage). • Build internal tools and software that make dataset creation, labeling, and curation faster and more reliable for the ML and labeling teams. • Coordinate closely with our labeling team to ensure data is well curated, consistently annotated, and error-free. • Improve how we collect and prioritize training data over time, including model-assisted and active-learning approaches.

• Design, build, and maintain batch, incremental, and file-based data pipelines to support analytics, reporting, and operational use cases. • Support data migration initiatives, including the movement of data from legacy systems to modern platforms, ensuring continuity, accuracy, and reconciliation. • Work with enterprise data integration tools such as Talend, including maintaining existing Talend jobs and supporting the migration or re-implementation of Talend pipelines into other platforms where required. • Establish and maintain a robust data engineering environment (e.g., dev/test/prod separation, access controls, naming standards, source control, deployment processes, and monitoring) that enables reliable delivery and safe change management. • Support ingestion, storage, and management of unstructured and semi-structured datasets (e.g., documents, PDFs, text extracts, files, metadata) alongside traditional relational data. • Support and optimize data models in Power BI dashboards and AI-enabled analytics. • Prepare and curate analytics- and AI-ready datasets, including clean, well-defined tables and reference data used in automation and AI-enabled solutions. • Collaborate closely with AI & Automation Specialists to ensure data requirements for AI initiatives are well understood, properly sourced, validated, and production-ready. • Translate business and reporting requirements into scalable data models, transformations, and pipeline designs. • Develop and maintain trusted datasets that support dashboards, scorecards, client reporting, and downstream analytics. • Implement data quality checks, validation logic, reconciliation processes, and monitoring to ensure data reliability and consistency. • Troubleshoot and resolve data issues across ingestion, transformation, and consumption layers, identifying root causes and remediation actions. • Develop and maintain clear documentation, including source-to-target mappings, data definitions, data lineage, and known limitations. • Work in partnership with analytics, IT, data governance, and security stakeholders to ensure data solutions comply with privacy, security, and regulatory expectations. • Contribute to the prioritization, planning, and delivery of multiple data engineering, migration, and enablement initiatives in parallel.

• Collaborate with analytics and business teams to improve data models that feed business intelligence tools, increasing data accessibility and fostering data-driven decision making across the organization. • Build and implement scalable solutions that align to our data governance standards and architectural roadmap for data integrations, data storage, reporting, and analytic solutions. • Design, develop and test data integration solutions. Write, automate and document unit/integration/functional tests. • Manage automated deployments using Git pipelines, ensuring code changes are efficiently version-controlled and deployed. • Collaborate with multiple teams to streamline deployment workflows, monitor and troubleshoot pipelines for reliability and performance. • Perform data analysis required to troubleshoot data-related issues by building a data quality framework and assist in the resolution of data issues. • Serve as tech lead by mentoring less experienced members of the team through code reviews, pair programming and similar hands-on interactions.