Design and implement scalable cloud data platforms and pipelines. Build and maintain ingestion, transformation, orchestration workflows. Architect and optimize Snowflake data warehouses, tune SQL for large datasets, implement Terraform automation, and mentor developers while contributing hands-on to backend data processing.
We are looking for a Data Engineering Architect who can contribute both architecturally and hands‑on in building scalable data platforms. The ideal candidate should have strong expertise in data engineering and backend development, with a focus on Snowflake, Airflow, SQL performance tuning, and modern cloud-based data pipelines. This role requires someone who can design solutions, guide team members, and actively contribute to coding and implementation. Key Responsibilities Design and implement scalable data engineering solutions and data pipelines. Develop and maintain data ingestion, transformation, and orchestration workflows. Architect and optimize Snowflake-based data warehouse solutions. Perform SQL query optimization and performance tuning for large-scale datasets. Work with engineering teams to build robust backend data processing frameworks. Guide and mentor developers while also contributing hands-on coding. Implement infrastructure and deployment automation using Terraform. Ensure best practices in data architecture, governance, and pipeline reliability. Collaborate with cross-functional teams including analytics, engineering, and platform teams. Mandatory Skills (Non‑Negotiable) Snowflake SSIS (SQL Server Integration Services) Azure Cloud Terraform Python or Java Preferred / Good-to-Have Skills Apache Airflow for workflow orchestration Advanced SQL and performance tuning Experience with data pipeline orchestration and automation Understanding of data warehouse design and optimization Exposure to Power BI or BI data modeling
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