Data Engineer

We are seeking a highly technical Senior Data Engineer with deep expertise in PostgreSQL and modern data engineering practices-and with AI enablement as a core, must have skill. In this role, you will modernize our data platform, refactor complex database centric business logic, and build high quality data pipelines that serve as the foundation for advanced analytics and AI workloads. This is a senior, hands-on individual contributor role with substantial ownership of coding, database architecture, and production reliability., * Modernize and refactor complex business logic currently embedded in Postgres functions, preparing systems for advanced AI integration.

• Design scalable schemas and AI-ready data models optimized for analytics, machine learning, and large-scale data processing.
• Implement database partitioning, indexing, and tuning strategies to support data growth, performance, and reliability.
• Design, build, and own production-grade data pipelines-from ingestion to consumption-capable of supporting real-time and batch AI use cases.
• Drive data quality, testing, monitoring, and operational reliability across pipelines to ensure trustworthy inputs for AI models.
• Work directly in production systems to troubleshoot, diagnose, and resolve data and performance issues.

• PostgreSQL expertise is mandatory, including advanced SQL, performance tuning, and production-scale experience.
• Demonstrated experience enabling or supporting AI/ML workloads as part of a data platform.
• 5+ years of hands-on experience in data engineering, backend engineering, or a related field.
• Experience refactoring, modernizing, or decomposing legacy database-centric architectures.
• Proficiency in Python or a comparable data engineering language.
• Strong understanding of ETL/ELT pipelines, data modeling, performance optimization, and data reliability engineering., * Experience designing features, pipelines, or architectures specifically supporting machine learning or AI applications.
• Familiarity with orchestration tools (e.g., Airflow, Dagster) or cloud data ecosystems.
• Experience implementing automated data quality checks, pipeline testing, or CI/CD for data workflows.