Data Engineer

We are seeking an experienced Data Engineer with a deep foundation in SQL, ETL development, and enterprise data warehousing. In this role, you will design, build, and optimize scalable data infrastructure to support complex analytics, machine learning, and business intelligence. You will architect modern cloud data topologies, work with both batch and streaming pipelines, and design schemas tailored for both traditional reporting and modern AI-driven workloads., * Pipeline & Scripting Development: Design, develop, and deploy Python scripts and robust ETL/ELT processes to prepare structured, semi-structured, and unstructured data for analysis.

• Data Modeling: Model dimensional (star/snowflake) and denormalized schemas optimized for performant enterprise reporting, discovery, and analytics.
• AI & Knowledge Architecture: Design AI-friendly database schemas, ontologies, and data structures to power advanced analytics and AI applications.
• Cloud Architecture & Topology: Architect enterprise cloud ops solutions for data topologies, working across cloud-based data environments.
• Real-Time Data Processing: Implement and manage event-based and streaming technologies for real-time data ingestion and processing.
• Optimization & Performance: Tune ETL jobs and SQL queries for maximum performance and scalability to seamlessly handle big data workloads.
• Maintenance & Troubleshooting: Proactively monitor pipelines, identify bottlenecks, and resolve operational issues to ensure high data reliability.
• Database Programming & Reverse Engineering: Write advanced SQL queries and stored procedures while reverse engineering existing data pipelines to improve architecture.
• Quality Assurance & Standards: Perform rigorous code reviews to ensure alignment with business requirements, optimal execution patterns, and architectural standards.
• DevOps & Release Management: Support automated release management and continuous integration/continuous deployment (CI/CD) processes.
• Data Quality & Governance: Validate and cleanse incoming data while designing fault-tolerant pipelines that handle error conditions gracefully.

• 5+ years writing complex SQL queries and working with relational database management systems (RDBMS).
• 5+ years of hands-on experience developing, deploying, and maintaining production-grade ETL/ELT pipelines.
• Demonstrated experience with cloud-based data warehouses and environments (e.g., Snowflake, AWS Redshift, AWS RDS).
• Strong understanding of data warehouse design principles, including OLTP vs. OLAP, Fact and Dimension modeling, and denormalization.
• Hands-on experience with cloud-based data architectures, messaging protocols, and enterprise analytics platforms.
• Education: Bachelor’s degree in Computer Science, Information Systems, Software Engineering, or equivalent experience preferred.

Preferred Qualifications (Pluses)

• Strong proficiency with Python, dbt, and Pandas for data transformation.
• Containerization experience using Docker and Kubernetes.
• Experience building and maintaining CI/CD pipelines for data infrastructure.
• Familiarity with serverless cloud workflows (e.g., AWS Lambdas, Step Functions).
• Experience with advanced big data techniques, including data partitioning and performance tuning.
• Active Cloud Certifications (e.g., AWS Certified Data Engineer, Snowflake Certified, GCP Data Engineer).