Senior Data Warehouse Engineer

Accountabilities:: Architect, build, and maintain scalable, high-performance data warehouse solutions that support analytics and operational needs. Develop reliable, well-documented data pipelines and ELT processes while maintaining strong standards for data quality and consistency. Optimize data warehouse performance through query optimization, partitioning, indexing, and other performance-tuning techniques. Partner with data analysts and data scientists to transform raw data into actionable analytics and support machine learning initiatives. Monitor, troubleshoot, and continuously improve data warehouse infrastructure while balancing performance, reliability, and cost efficiency. Establish and promote best practices for clean, well-structured, reliable, and accessible data. Work with modern cloud-based data warehouses such as ClickHouse and integrate data platforms with other cloud services. Contribute to developer-facing data tools and solutions that empower engineering and other technical teams. Support data transformation and modeling initiatives using tools such as dbt and SQL. Contribute to automation, testing, version control, code reviews, and other software engineering practices across data systems. Help improve the scalability, maintainability, and operational efficiency of the overall data platform. Requirements: Extensive professional experience designing, implementing, and optimizing data warehouses in cloud environments such as AWS or GCP. Strong hands-on expertise with data modeling and transformation tools, particularly dbt. Excellent SQL skills and experience with modern columnar or cloud data warehouse technologies such as ClickHouse, Databricks, BigQuery, Snowflake, or Redshift. Experience developing and managing data pipelines using orchestration tools such as Prefect or Airflow. Solid understanding of data engineering principles, warehouse architecture, transformation, and data quality. Strong software engineering fundamentals, including version control, code reviews, testing, automation, and maintainable development practices. Experience working collaboratively with data scientists, analysts, engineers, and other cross-functional stakeholders. Strong English communication skills for effective collaboration within a globally distributed remote team. High degree of ownership, autonomy, and accountability, with the ability to work effectively in ambiguous environments. Strong analytical and problem-solving skills, with a practical approach to troubleshooting and performance optimization. Familiarity with business intelligence and visualization tools such as Apache Superset, Sigma, Tableau, or Looker is a plus. Experience with infrastructure-as-code tools such as Terraform and a DevOps-oriented mindset is desirable. Scripting or automation experience with Bash, Python, or Go is an advantage. Experience with technologies such as Kafka, AWS, Terraform, and modern data visualization platforms is beneficial. Benefits: Fully remote work arrangement within eligible hiring locations. Opportunity to work on large-scale data infrastructure supporting a technology platform focused on online security and fraud detection. Exposure to a modern technology stack including ClickHouse, Redshift, Confluent Kafka, dbt, Prefect, AWS, Terraform, Apache Superset, and Sigma. Opportunity to collaborate with data scientists, analysts, engineers, and globally distributed technical teams. High level of ownership and autonomy in a data engineering environment. Opportunity to contribute to developer-facing tools and scalable data platform initiatives. US-based compensation range of $152,000–$205,000; compensation may vary by hiring location and is not directly applicable to all locations. Fully remote international environment with team members distributed across multiple countries and time zones. Visa sponsorship is not provided; candidates must be authorized to work from their home location. Specific India-based salary, healthcare, retirement, paid time off, and other benefits were not specified in the source description.