• Lead the development and implementation of our data engineering strategy, architecture roadmap, and technical standards. • Oversee the design and evolution of our data ecosystem utilizing Data Vault methodologies and Data Mesh principles. • Establish governance and quality frameworks across Bronze (raw), Silver (transformed), and Gold (consumption-ready) data layers. • Partner with Product Management to align data platform capabilities with business objectives and market demands. • Provide technical leadership and oversight for the data engineering team. • Oversee the design and implementation of Snowflake data architecture including warehousing, marts, and access patterns. • Direct the development of robust ETL/ELT processes using Matillion and other modern data integration tools. • Guide the implementation of data quality monitoring, lineage tracking, and metadata management. • Build, mentor, and lead a high-performing team.

Important Information Location: Brazil Job Mode: Full-time Work Mode: Work from home Job Summary We are seeking a highly experienced Data Engineer Lead to architect, develop, and deliver robust, scalable, end-to-end automation solutions. This role demands deep technical expertise across solution architecture, environment setup, deployments, observability, governance, and continuous improvement. The ideal candidate is a strong technical leader and communicator who partners closely with business stakeholders to define automation strategies and deliver high-impact, enterprise-grade solutions. Responsibilities and Duties - Lead and mentor a team of data engineers, providing technical guidance, code reviews, and best practices to ensure high-quality deliverables; - Own the end-to-end data migration strategy from Snowflake on AWS to Azure Databricks and DBT, including planning, execution, and optimization; - Collaborate with cross-functional stakeholders (data, product, architecture, and business teams) to align data initiatives with organizational goals; - Architect and oversee scalable, secure, and reliable data pipelines and infrastructure in Azure; - Drive the design and implementation of infrastructure-as-code using Terraform, ensuring consistency and maintainability; - Ensure efficient data integration between Snowflake and Azure environments, establishing robust and scalable data flows; - Oversee data transformation frameworks using Databricks and DBT, promoting reusable and modular design patterns; - Establish and enforce data engineering standards, governance practices, and CI/CD workflows; - Proactively identify risks, troubleshoot complex technical issues, and implement long-term solutions; - Lead documentation efforts for architecture, processes, and best practices across the data platform. Essential Skills - Experience in Data Engineering; - Proven experience leading data engineering teams and delivering large-scale data platform initiatives; - Strong hands-on experience with Snowflake, Azure Databricks, and DBT; - Deep expertise in cloud infrastructure and infrastructure-as-code tools, especially Terraform; - Extensive experience with cloud data migration projects (AWS to Azure preferred); - Strong experience with CI/CD pipelines and Git-based version control; - Advanced proficiency in SQL, data modeling, and ETL/ELT design patterns; - Strong understanding of data architecture, scalability, and performance optimization; - Ability to operate in a fast-paced, collaborative, and evolving environment; - Mandatory experience in the insurance industry, with understanding of insurance data domains (policies, claims, underwriting, etc.). Highly Desirable Skills - Experience with data governance frameworks (data cataloging, lineage, quality, and observability tools); - Knowledge of security and compliance standards relevant to insurance and financial data; - Familiarity with streaming and real-time data processing (Kafka, Event Hubs, Delta Live Tables); - Experience designing lakehouse architectures and implementing cost-optimization strategies; - Exposure to multi-cloud environments and advanced automation practices. About Encora Encora is the preferred digital engineering and modernization partner of some of the world’s leading enterprises and digital native companies. With over 9,000 experts in 47+ offices and innovation labs worldwide, Encora’s technology practices include Product Engineering & Development, Cloud Services, Quality Engineering, DevSecOps, Data & Analytics, Digital Experience, Cybersecurity, and AI & LLM Engineering. At Encora, we hire professionals based solely on their skills and qualifications, and do not discriminate based on age, disability, religion, gender, sexual orientation, socioeconomic status, or nationality.

• Develop and maintain metadata-driven data pipelines for ingestion, transformation, and delivery of data • Build and support ETL/ELT processes for both legacy and modern data sources • Implement data transformations to convert legacy formats (e.g., SFTP, flat files) into modern formats (e.g., XML, JSON, APIs) • Develop API integrations and data services supporting real-time and near real-time data exchange • Support implementation of data quality checks, validation rules, and monitoring processes • Collaborate with a Data Architect to implement data models, schemas, and integration patterns • Optimize pipelines and queries for performance, scalability, and reliability • Assist in documenting data flows, interfaces, and pipeline configurations • Support DevSecOps practices to ensure a standardized data environment, including implementing CI/CD pipelines, automated testing, and deployment • Work in cross-functional team with data scientists, data engineers, and business stakeholders

Role Description We are currently building a next-generation Data Fabric—a multi-tenant, high-performance platform built on Databricks that will serve as the substrate for all Kinaxis applications. We aren’t looking for an integrator to build pipelines; we need a Systems Architect to build the engine. - Architect the Fabric: Lead the technical vision for a Databricks-centric platform, focusing on Delta Sharing, Unity Catalog, and custom data orchestration. - Solve for "The Platform": Move beyond simple ingestion. You will design the "low-level" primitives—identity, compute quotas, and automated RBAC—that make the platform scalable for thousands of external users. - Bridge the Gap: Translate complex infrastructure constraints (Kubernetes, Azure/GCP, Databricks) into a seamless developer experience for our internal and external application teams. - Direct Impact: Your contributions will have direct impact on the Kinaxis platform. Qualifications - 10+ years of experience as a Systems Thinker. - Deep hands-on experience with Databricks, Spark, and Delta Lake. - Fluency in Kubernetes, containerization, and the networking/security layers of Azure or GCP. - Ability to debate long-term architectural strategy and engage in complex debugging sessions. - Experience in finding opportunities to accelerate the SDLC through innovative applications of AI. Requirements - Understanding of the difference between a solution and a scalable platform. - Ability to review AI-generated code for correctness, architectural fit, integration risk, and edge case support. Benefits - Flexible vacation and Kinaxis Days (company-wide day off on the last Friday of every month). - Flexible work options. - Physical and mental well-being programs. - Regularly scheduled virtual fitness classes. - Mentorship programs and training and career development. - Recognition programs and referral rewards. - Hackathons.