• Design and implement reusable data platforms and systems with a focus on security • Define architectural standards for data flows, ensuring scalability and resilience • Lead the development of data ingestion, processing, and governance pipelines across cloud and on-premises environments • Propose and evolve integration solutions between dependent teams using modern data tools • Implement best practices for observability, cost control, and security in distributed environments • Support the dissemination of development standards, code review practices, and release automation

Role Description We are looking for an Analytics Consultant with sound experience in the development of data integration solutions. SQL and working knowledge of Data Integration techniques necessary (ETL, Replication, Profiling) to support business needs. - Develop and maintain SQL code and SSIS packages. - Analyze data and solve new and existing business issues. - Reviewing query performance and optimizing code. - Provide production level support. - Fully document all processes that are being created. - Sound experience on Microsoft on-premise data integration stack (SSIS, SQL, SQL Server). - Skilled in PowerBI. - Fluent in English, able to communicate both with the team and client. - Knowledge of insurance industry is a plus. - Reside in Argentina. Qualifications - Sound experience in data integration solutions. - Proficiency in SQL and Data Integration techniques (ETL, Replication, Profiling). - Experience with Microsoft on-premise data integration stack (SSIS, SQL, SQL Server). - Skilled in PowerBI. - Fluent in English. - Knowledge of the insurance industry is a plus. - Must reside in Argentina. Requirements - Experience developing and maintaining SQL code and SSIS packages. - Ability to analyze data and solve business issues. - Experience in reviewing query performance and optimizing code. - Capability to provide production level support. - Ability to fully document processes.

• Design, develop, and maintain enterprise data architecture solutions that support business growth, operational efficiency, regulatory compliance, and risk management objectives. • Lead modernization initiatives across data platforms, integration frameworks, reporting environments, and analytical capabilities. • Evaluate and implement emerging technologies, including cloud services, artificial intelligence, machine learning, automation, and advanced analytics solutions where appropriate. • Architect scalable and reusable data integration frameworks that reduce maintenance overhead and support future business expansion. • Develop and optimize ETL/ELT processes, data pipelines, and data movement strategies across multiple systems and platforms. • Collaborate with business leaders, risk management teams, compliance personnel, and technology stakeholders to translate business requirements into sustainable technical solutions. • Design data models, metadata strategies, governance frameworks, lineage tracking, and quality controls that support enterprise reporting and regulatory requirements. • Partner with stakeholders to identify opportunities for process automation, operational improvement, and enhanced decision-making through data-driven solutions. • Establish and promote development standards, architectural best practices, and data engineering disciplines that balance agility with long-term maintainability. • Evaluate existing systems and processes to identify technical debt, scalability limitations, operational risks, and modernization opportunities. • Support risk modeling, forecasting, financial analytics, and strategic reporting initiatives through robust data architecture and engineering practices. • Implement monitoring, validation, reconciliation, and control processes to ensure data accuracy, integrity, availability, and auditability. • Participate in technology roadmap planning and provide architectural guidance for future-state platform evolution. • Serve as a technical leader and trusted advisor across multiple business and technology teams. • Maintain awareness of regulatory expectations, industry trends, cybersecurity considerations, and emerging technologies impacting financial services organizations.

• Design and develop CLI tools, scripts, and internal utilities to automate repetitive tasks across the data platform, including: • Pipeline execution and orchestration • Data governance workflows • Metadata synchronization • Environment setup and configuration • Test harness development • Automate workflows on Databricks, including: • Job deployment and scheduling • Environment provisioning • MLOps processes using APIs, Terraform, or Databricks SDK • Build and implement robust testing frameworks: • Integration testing for pipelines • End-to-end validation of ETL/ELT workflows • Testing and validation for ML inference workflows • Improve overall productivity, scalability, and reliability of the data and ML engineering ecosystem • Develop lightweight internal tools and dashboards using frameworks such as React, Streamlit, or similar technologies to: • Visualize data pipelines and workflows • Demonstrate model inference capabilities • Provide configuration and operational controls • Enable internal productivity monitoring and dashboards • Collaborate with cross-functional teams to identify automation opportunities and implement best practices