Data Engineer

Job Description This is a remote position. We are looking for Data Engineers (Junior, Mid or Senior)! At KIS, we are always looking for talented individuals to join our team for future opportunities. If you are a Data Engineer and interested in working on innovative projects with one of our global clients, sign up for our Talent Pool! Main Responsibilities: Design, build, and maintain end-to-end data pipelines (batch and/or streaming), from ingestion to transformation and delivery. Develop and operate ETL/ELT workflows, ensuring reliability, scalability, and performance. Write efficient, production-grade SQL queries for data extraction, transformation, and analytics use cases. Implement and maintain data models (e.g., star schemas, incremental models) optimized for analytics and reporting. Develop reusable and modular Python code for data transformations and pipeline logic. Monitor data pipelines, troubleshoot failures, and perform root cause analysis across code, orchestration, data sources, and cloud services. Ensure data quality by implementing automated validation checks (schema validation, freshness checks, row-level assertions). Translate business and analytical requirements into robust technical data solutions. Collaborate with analysts, backend engineers, and other stakeholders to define data contracts and ensure data availability. Actively participate in planning, estimation, and prioritization of data engineering tasks. Proactively identify risks related to performance, scalability, or data integrity and propose mitigation strategies. Contribute to continuous improvement of data platforms, processes, and team practices. Write and maintain technical documentation for pipelines, schemas, and data lineage. Communicate clearly with team members and clients, raising questions and concerns when requirements or priorities are unclear. Support and mentor other team members when appropriate, contributing to overall team delivery. Requirements Professional experience as a Data Engineer working with production data pipelines. Strong experience with SQL, including query optimization, indexing, partitioning, and performance trade-offs. Professional experience writing Python for data transformations, following good design and modularization practices. Experience designing and implementing data models for analytics use cases. Experience building and operating pipelines using cloud-based data platforms. Hands-on experience with Azure, Databricks, and Data Lake environments. Experience operating data pipelines, including error handling, monitoring, and data quality processes. Familiarity with Git for version control, including branching and resolving merge conflicts. Experience working with Kubernetes or containerized data workloads. Understanding of data formats such as Parquet or ORC, including cost and performance considerations. Knowledge of basic data security and governance practices (access control, masking, PII handling). Ability to deliver less complex tasks independently and more complex tasks with guidance. Strong sense of ownership, responsibility, and accountability for data workflows. Good organizational and time management skills, with the ability to estimate and meet delivery deadlines. Advanced English for collaboration with global clients. Team-oriented mindset with strong communication and problem-solving skills. Live in Latin America region.​ Nice to Have Experience with data orchestration tools (e.g., Airflow, Azure Data Factory, or similar). Exposure to CI/CD for data pipelines and deployment automation. Experience with streaming data (e.g., Kafka, Event Hubs). Familiarity with data observability and monitoring tools. Experience collaborating with Machine Learning or advanced analytics teams. Experience working with Java Spring Boot in data engineering projects.