> Markdown version of [/jobs/ext/3686615-big-data-architect](https://www.wearedevelopers.com/jobs/ext/3686615-big-data-architect). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Big Data Architect - **Company:** Bright Vision Technologies - **Location:** Fishers, IN, United States (Remote available) - **Experience:** Expert - **Salary:** $100,000.0 - $150,000.0 - **Contract:** Permanent contract - **Skills:** Amazon Web Services, Microsoft Azure, Big Data, BigQuery, Cloud Computing, Information Engineering, Data Infrastructure, Data Systems, Data Mesh, Snowflake, Apache Spark, Data Layers, Apache Kafka, Data Management, Video Streaming, Data Pipelines, Amazon Redshift, Databricks - **Published:** October 11, 2026 - **Apply:** https://www.careerjet.com/jobad/us5dd48610de14e32ec39153f56ff7cd03 ## About the Role * 8+ years of experience in data engineering, including architecture roles. * Strong expertise in Snowflake, Databricks, BigQuery, Redshift, Spark, Kafka, and cloud platforms. * Experience with data modeling, lakehouse architectures, governance, and streaming technologies. Preferred Qualifications * Experience with data mesh, dbt, LookML, or semantic layers. * Cloud certifications (Snowflake, Databricks, AWS, Azure, or GCP) and experience in regulated environments. ## Description We are seeking an experienced Big Data Architect to design and lead enterprise-scale data platforms. The ideal candidate will have expertise in Snowflake, Databricks, BigQuery, Redshift, Spark, Kafka, lakehouse architectures, and cloud platforms, with experience building scalable, secure, and high-performance data solutions. Key Responsibilities * Design and lead enterprise data platform architecture and data pipelines. * Build scalable lakehouse, warehouse, and streaming solutions. * Define data modeling, governance, security, and architecture standards. * Optimize platform performance, scalability, reliability, and cost. * Collaborate with data engineering, analytics, ML, and business teams. * Mentor engineers and drive data platform best practices.