Big Data Lead

We’re seeking a Data Engineer with strong Snowflake, Airflow, and SQL expertise to design, build, and optimize scalable data pipelines and analytics platforms. You will own end-to-end ELT/ETL workflows, ensure data quality and reliability, and drive continuous performance and cost optimization. Key Responsibilities Build and maintain ELT/ETL pipelines in Airflow (DAGs, scheduling, monitoring). Design and optimize Snowflake data models, warehouses, and transformations. Write high-quality SQL for complex transformations and analytics use cases. Implement data quality checks, observability, and SLAs/alerts. Optimize performance and cost (warehouses, micro-partitions, clustering, resource monitors). Collaborate with analytics, product, and platform teams; document and automate. Required Qualifications Advanced SQL (CTEs, window functions, performance tuning). Snowflake hands-on (virtual warehouses, stages, external tables, Snowpipe, Streams & Tasks, Time Travel, cloning, RBAC). Airflow 2.x (TaskFlow API, operators/sensors, retries, XComs, DAG best practices). Python for pipeline orchestration and Airflow development. Experience with at least one cloud (AWS, GCP, or Azure) object storage and IAM. CI/CD and version control (Git), code reviews, and environment management. Nice to Have dbt, Terraform/IaC, Docker/Kubernetes for Airflow. Streaming/CDC (Kafka, Kinesis, Pub/Sub, Fivetran, Debezium). Data observability (e.g., Great Expectations), lineage/governance, masking/row access policies. Cost governance and security best practices (PII handling, encryption, compliance).