Designs, builds, and maintains scalable batch and streaming data pipelines, ETL/ELT workflows, data models, and Python transformations. Writes optimized SQL, monitors pipeline reliability, troubleshoots failures, implements data-quality validation, and supports security and governance practices. Collaborates with engineers, analysts, stakeholders, and clients to define data contracts, improve platforms, document lineage, and deliver reliable analytics data.
This is a remote position.
About KIS
KIS is a global technology consultancy, 100% remote and headquartered in the United States. We partner with companies ranging from innovative startups to large multinational organizations, helping them build modern, scalable, and high-impact technology solutions.
Our global team works on challenging projects across different industries, combining technical excellence, innovation, and close collaboration with our clients.
We are currently looking for a Mid-Level Data Engineer to join our team and work on a challenging project for one of KIS's leading international clients.
As a Mid-Level Data Engineer, you will be responsible for designing, building, and maintaining reliable and scalable data solutions. You will work closely with engineers, analysts, and stakeholders to ensure high-quality, accessible, and well-structured data across the organization.
Your responsibilities will include:
- Design, build, and maintain end-to-end data pipelines, including batch and/or streaming workflows, from ingestion through transformation and delivery.
- Develop and operate reliable, scalable, and high-performing ETL/ELT workflows.
- Write efficient, production-grade SQL queries for data extraction, transformation, and analytics use cases.
- Implement and maintain data models, including star schemas and incremental models, optimized for analytics and reporting.
- Develop reusable, modular, and maintainable Python code for data transformations and pipeline logic.
- Monitor data pipelines, troubleshoot failures, and perform root cause analysis across code, orchestration tools, data sources, and cloud services.
- Ensure data quality by implementing automated validation checks, including schema validation, freshness checks, and row-level assertions.
- Build AI Agents to streamline data platforms, engineering processes and reporting.
- Collaborate with analysts, backend engineers, and other stakeholders to define data contracts and ensure reliable data availability.
- Actively participate in planning, estimation, and prioritization of data engineering tasks.
- Proactively identify risks related to performance, scalability, and data integrity, proposing effective mitigation strategies.
- Contribute to the continuous improvement of data platforms, engineering processes, and team best practices.
- Write and maintain clear technical documentation for data pipelines, schemas, and data lineage.
- Communicate clearly with team members and clients, proactively raising questions and concerns when requirements or priorities are unclear.
Requirements
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