Senior Data Engineer

About the Company

Gemini is a global crypto and Web3 platform founded by Cameron and Tyler Winklevoss in 2014, offering a wide range of simple, reliable, and secure crypto products and services to individuals and institutions in over 70 countries. Our mission is to unlock the next era of financial, creative, and personal freedom by providing trusted access to the decentralized future. We envision a world where crypto reshapes the global financial system, internet, and money to create greater choice, independence, and opportunity for all — bridging traditional finance with the emerging cryptoeconomy in a way that is more open, fair, and secure. As a publicly traded company, Gemini is poised to accelerate this vision with greater scale, reach, and impact.

The Department: Data

At Gemini, our Data Team is the engine that powers insight, innovation, and trust across the company. We bring together world-class data engineers, platform engineers, machine learning engineers, analytics engineers, and data scientists — all working in harmony to transform raw information into secure, reliable, and actionable intelligence. From building scalable pipelines and platforms, to enabling cutting-edge machine learning, to ensuring governance and cost efficiency, we deliver the foundation for smarter decisions and breakthrough products. We thrive at the intersection of crypto, technology, and finance, and we’re united by a shared mission: to unlock the full potential of Gemini’s data to drive growth, efficiency, and customer impact.

The Role: Senior Data Engineer

The Data team is responsible for designing and operating the data infrastructure that powers insight, reporting, analytics, and machine learning across the business. As a Senior Data Engineer, you will contribute to architectural decisions, mentor junior engineers, and build high-scale systems that have meaningful impact on your team and the teams you partner with. You will own the end-to-end delivery of data products within your domain, and partner closely with product, analytics, ML, finance, operations, and engineering teams to move, transform, and model data reliably, with observability, resilience, and agility.

Responsibilities:

Design, build, and maintain data infrastructure and pipelines spanning both batch and real-time / streaming workloads, contributing to architectural decisions along the way

Build and maintain scalable, efficient, and reliable ETL/ELT pipelines using languages and frameworks such as Python, SQL, Spark, Flink, Beam, or equivalents

Work on real-time or near-real-time data solutions (e.g. CDC, streaming, micro-batch) for use cases that require timely data

Partner with data scientists, ML engineers, analysts, and product teams to understand data requirements, define SLAs, and deliver coherent data products that others can self-serve

Establish data quality, validation, observability, and monitoring frameworks (data auditing, alerting, anomaly detection, data lineage)

Investigate and resolve complex production issues: root cause analysis, performance bottlenecks, data integrity, fault tolerance

Document data flows, data dictionaries, architecture patterns, and operational runbooks

Minimum Qualifications:

5+ years of experience in data engineering (or similar) roles

Strong experience in ETL/ELT pipeline design, implementation, and optimization

Deep expertise in Python and SQL writing production-quality, maintainable, testable code

Experience with large-scale data warehouses (e.g. Databricks, BigQuery, Snowflake)

Solid grounding in software engineering fundamentals, data structures, and systems thinking

Hands-on experience in data modeling (dimensional modeling, normalization, schema design)

Experience building systems with real-time or streaming data (e.g. Kafka, Kinesis, Flink, Spark Streaming), and familiarity with CDC frameworks

Experience with orchestration / workflow frameworks (e.g. Airflow)

Familiarity with data governance, lineage, metadata, cataloging, and data quality practices