Data Engineer

• This is a remote position.
• 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.
• Professional experience as a Data Engineer working with production-grade data pipelines.
• Strong experience with SQL , including query optimization, indexing, partitioning, and understanding performance trade-offs.
• Professional experience writing Python for data transformations, following good software design and modularization practices.
• Experience designing and implementing data models for analytics and reporting use cases.
• Experience building and operating data pipelines using cloud-based data platforms.
• Previous hands on experience building AI agents.
• Experience operating data pipelines, including error handling, monitoring, troubleshooting, and data quality processes.
• Knowledge of fundamental data security and governance practices, including access control, data masking, and PII handling.
• Ability to deliver less complex tasks independently and handle more complex challenges with appropriate guidance.

None flagged.