Data Engineer

Strong Databricks experience is required, along with advanced SQL skills., * Bachelor’s Degree preferred, or equivalent practical hands-on experience. \n

• A proven track record designing, delivering, and operating production-grade solutions in Databricks on AWS, including responsibility for architecture implementation, pipeline development, deployment, support, performance, and cost. \n

• 5 years of professional experience in data engineering, data platform engineering, analytics engineering, or similar. \n

• Proven experience delivering multiple end-to-end projects where engineered data products enabled sophisticated analysis - such as cross-system KPI reporting, forecasting, optimization, profitability analysis, anomaly detection, predictive modeling, scenario analysis, or AI/ML. \n

• Advanced SQL and strong Python/PySpark skills, including experience developing maintainable, tested, production-quality code for large and complex datasets. \n

• Strong knowledge of Spark, Delta Lake or comparable lakehouse patterns, dimensional and analytical data modeling, partitioning, schema evolution, data contracts, and performance tuning. \n

• Hands-on experience with core AWS data-platform capabilities such as S3, IAM, KMS, VPC networking, CloudWatch, Secrets Manager, and related services used to secure, integrate, and operate Databricks workloads. \n

• Experience building batch and incremental pipelines using APIs, files, databases, SaaS connectors, change data capture, and at least one orchestration framework or managed workflow capability. \n

• Experience with Git-based development, CI/CD, automated data testing, code review, environment management, and infrastructure-as-code practices. \n

• Experience implementing data quality, observability, cataloging, lineage, access control, and operational support for business-critical data products.