Senior Data Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Engineer based in United States. This is a high-impact opportunity to build the foundational data infrastructure supporting advanced analytics, risk management, and financial products. You’ll take end-to-end ownership of a modern data platform, consolidating fragmented on-chain and off-chain sources into a trusted ecosystem. The role combines hands-on data engineering with architectural decision-making, giving you significant influence over how data is collected, modeled, governed, and consumed. You’ll lead the migration of analytics workflows into BigQuery while developing reliable batch and streaming pipelines for production systems. Working closely with Data Science, Backend Engineering, Risk, and Product, you’ll turn complex analytical needs into scalable and reusable infrastructure. The environment values autonomy, urgency, technical excellence, and strong ownership, with a focus on correctness and long-term reliability. This is an ideal role for an experienced engineer who wants to shape a data foundation from the ground up in a fast-growing financial technology environment. Lead the migration of existing analytics workflows and data processes into BigQuery, establishing a unified and trusted warehouse and reporting layer. Design, build, test, and operate reliable ETL and ELT pipelines that enable data scientists, analysts, risk teams, and other stakeholders to work efficiently with trusted data. Read, understand, and extend production Go services to support additional data ingestion, including changefeeds and streaming or batch sinks. Consolidate data from internal operational databases, blockchain and market-data providers, and product analytics systems into consistent, well-modeled datasets. Establish and maintain standards for data ingestion, transformation, orchestration, testing, lineage, documentation, and data governance. Develop foundational metrics and dashboards in collaboration with Data Science and other business stakeholders. Own data quality, pipeline reliability, observability, monitoring, access controls, and warehouse cost optimization. Build infrastructure that enables self-service analytics and reduces dependence on fragmented or ad-hoc data workflows. Partner closely with Data Science, Backend Engineering, Risk, and Product teams to translate analytical and reporting requirements into durable technical solutions. Identify opportunities to improve the scalability, maintainability, and efficiency of the overall data platform. Requirements 5+ years of professional data engineering experience building and operating production-grade data pipelines and data warehouses. Strong hands-on experience with cloud data warehouses, with deep BigQuery expertise strongly preferred, including performance optimization, partitioning, and cost management. Advanced SQL and data modeling skills, with practical experience using modern transformation and orchestration technologies such as dbt, Airflow, or comparable platforms. Experience designing batch and streaming ingestion architectures, including CDC, changefeeds, event pipelines, and integrations from operational databases into analytical warehouses. Experience building data infrastructure for fintech, financial services, trading, or other high-integrity environments where data correctness, reliability, and auditability are critical. Strong understanding of data quality, testing, observability, monitoring, lineage, documentation, and production reliability. Strong ownership and engineering judgment, with the ability to independently identify problems and implement durable solutions. Clear written and verbal communication skills, with the ability to collaborate effectively with both technical and non-technical stakeholders. Experience reading or writing Go is a plus, particularly for engineers working with production services and