Role Description We are seeking a Principal Data Engineer to lead the design, development, and optimization of modern data platforms that enable advanced analytics, machine learning initiatives, and data-driven decision-making. This role requires a highly experienced engineer capable of architecting scalable data solutions, mentoring engineering teams, and driving best practices across data engineering initiatives. You will work closely with Data Scientists, Analysts, Product teams, and business stakeholders to transform complex data ecosystems into reliable, scalable, and secure platforms that generate meaningful business insights. Key Responsibilities - Design, build, and maintain large-scale data platforms and data architectures. - Lead the development of scalable and reliable ETL/ELT pipelines for batch and near real-time processing. - Architect cloud-native data solutions leveraging AWS, Azure, or GCP services. - Drive data modeling strategies using methodologies such as Star Schema, Snowflake Schema, and Data Vault. - Define and enforce data engineering best practices, coding standards, governance policies, and architectural guidelines. - Implement orchestration frameworks using tools such as Airflow, dbt, or similar technologies. - Optimize data pipelines for performance, scalability, reliability, and cost efficiency. - Collaborate with Data Scientists and Analytics teams to ensure high-quality, production-ready datasets. - Establish monitoring, observability, testing, and data quality frameworks. - Lead technical discussions and architectural decisions across multiple teams. - Conduct code reviews and mentor Data Engineers across different seniority levels. - Implement data security, privacy, and compliance standards aligned with industry best practices. - Support strategic initiatives involving analytics, machine learning, and marketing intelligence platforms. Qualifications - 8+ years of experience in Data Engineering, Data Platforms, or Data Architecture roles. - Experience operating in Senior, Lead, Staff, or Principal Data Engineering positions. - Proven track record designing and implementing enterprise-scale data solutions. - Experience working in distributed and cloud-native environments. Technical Skills - Expert-level SQL skills. - Strong Python development experience for data engineering and processing. - Extensive experience building ETL/ELT pipelines. - Hands-on experience with Airflow, dbt, or equivalent orchestration tools. - Strong expertise in data modeling and warehouse design. - Experience with modern cloud platforms: AWS, Azure, GCP. - Experience with data lakes and data warehouses. - Knowledge of CI/CD practices for data platforms. - Understanding of data governance, security, lineage, and privacy controls. - Familiarity with analytics and machine learning data preparation workflows. Soft Skills - Strong ownership mentality. - Excellent communication and stakeholder management skills. - Ability to lead technical initiatives and influence engineering decisions. - Mentoring and coaching capabilities. - Strategic problem-solving mindset. - Adaptability in fast-paced environments. - Strong collaboration skills across technical and business teams. Education - Bachelor's degree in Computer Science, Software Engineering, Information Systems, Data Science, or related field. - Master's degree is a plus. Language - Advanced English (required). - Ability to participate in technical discussions and stakeholder meetings with international teams. Location - LATAM. - Mexico. - Remote position. Benefits - Integration with global brands and disruptive startups. - Remote work/Home office. - If a hybrid or on-site modality is required, you will be informed from the first interview session. - Schedule aligned with the assigned project/work cell. - Monday to Friday work schedule. - Birthday day off. - Major medical insurance (applies to Mexico). - Life insurance (applies to Mexico). - Multicultural work environments. - Access to courses and certifications. - IT meetups with special guests. - Virtual integration events and interest groups. - English classes. - Opportunities across our different business lines. - Proudly certified as a Great Place to Work.

• Responsible for leading the modernization, optimization, and stabilization of the Wisconsin Medicaid Market's data platform ecosystem. • Own the market's Data Warehouse and ODS, drives ETL/data movement strategy (including SSIS modernization). • Partners closely with the Market BI team to improve data access and flow. • Owns and evolves the Wisconsin Medicaid Market's data stores and data movement ecosystem, including the Data Warehouse and ODS. • Accountable for modernizing and optimizing the market's data platform to improve reliability, reduce technical debt, strengthen observability/fault tolerance, and increase engineering efficiency.

• Design, build, and maintain scalable streaming data architectures using Kafka, MSK, and Kinesis • Develop real-time data pipelines that handle high-volume, high-velocity data streams • Implement event-driven architectures and microservices patterns for streaming data processing • Create and optimize data streaming topologies for complex event processing scenarios • Design fault-tolerant streaming systems with proper error handling and data recovery mechanisms • Configure, deploy, and manage Apache Kafka clusters and AWS MSK environments • Implement Kafka Connect pipelines for streaming data integration • Design optimal Kafka topic partitioning strategies and replication configurations • Monitor and optimize Kafka cluster performance, throughput, and latency • Implement Kafka security configurations including SSL/TLS, SASL, and ACLs • Manage Kafka Schema Registry for data serialization and evolution • Design and implement Amazon Kinesis Data Streams and Kinesis Data Firehose solutions • Configure Kinesis Analytics applications for real-time stream processing • Optimize Kinesis shard management and auto-scaling configurations • Implement Kinesis data retention and archival strategies • Integrate Kinesis with other AWS services for comprehensive streaming solutions • Develop real-time stream processing applications using Apache Spark Streaming, Kafka Streams, or AWS Lambda • Implement complex event processing (CEP) patterns for real-time analytics • Build streaming ETL pipelines that transform data in motion • Create real-time aggregations, windowing operations, and stateful stream processing • Optimize streaming query performance and resource utilization • Ensure seamless integration between streaming systems and data lakes, data warehouses, and operational databases • Implement data lineage and monitoring for streaming data pipelines • Create automated data quality checks and validation for streaming data • Manage data serialization formats (Avro, JSON, Protobuf) and schema evolution • Coordinate with data scientists and analysts to ensure streaming data meets analytical requirements • Implement Infrastructure as Code (IaC) for streaming data platforms using Terraform or CloudFormation • Automate deployment and management of streaming infrastructure through CI/CD pipelines • Monitor streaming system health, performance metrics, and alerting • Implement disaster recovery and high availability strategies for streaming systems • Stay current with emerging trends in streaming technologies and cloud-native solutions • Collaborate with data architects, data scientists, and application teams on streaming data requirements • Support rigorous project governance through daily progress reviews and time tracking • Provide technical leadership and mentorship to junior data engineers • Communicate complex streaming concepts to technical and non-technical stakeholders • Operate with transparency and responsiveness to support high-performing teams.

• Design and implement scalable data platforms and pipelines across cloud environments • Develop reliable batch, streaming, and near-real-time pipelines • Build ingestion, transformation, and curation workflows for both structured and unstructured data • Implement modern data architectures including lakehouse patterns and medallion layering • Deliver high-quality datasets that support analytics, machine learning, causal modeling, and optimization systems • Design scalable logical and physical data models • Orchestrate workflows using tools such as Airflow, dbt, Lakeflow, or equivalents • Apply modern architecture patterns including event-driven and streaming architectures • Establish strong data observability • Enable data serving layers to support downstream systems • Collaborate with data scientists, ML engineers, analysts, and business stakeholders