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.

• Design, build, and maintain end‑to‑end data pipelines and integrations to support Medicine & Analytics use cases (batch and event‑driven patterns where relevant). • Develop, operate, and optimize integrations using SnapLogic and AWS services such as S3, AWS Lambda, and AWS Glue to ensure robust ingestion, transformation, and orchestration. • Implement and maintain analytics‑ready data models in Snowflake, ensuring performance, scalability, and cost‑efficient design. • Build transformation logic and analytics layers using dbt, including modular modeling, testing, documentation, and deployment best practices. • Contribute to and enforce data governance standards by leveraging tools such as Collibra, ensuring metadata quality, lineage, ownership, and consistent definitions. • Partner with Data Quality stakeholders to implement and monitor quality controls using Attaccama, including rules, profiling, exception handling, and remediation workflows. • Support data lifecycle processes and operationalization of data products using Innovator (as applicable in the ecosystem) to align delivery with platform and product standards. • Proactively identify opportunities to simplify architecture, automate repetitive work, and reduce operational effort (observability, alerting, self‑healing patterns). • Ensure all solutions follow security, privacy, and compliance expectations (e.g., regulated environment practices, audit readiness, access controls, data handling). • Collaborate closely with Product Owners, Data Scientists, Analysts, Architects, and business stakeholders to translate needs into reliable, reusable data assets. • Act as a role model for engineering excellence: version control, CI/CD, code reviews, documentation, and operational runbooks.