Senior Manager, Data Engineering

Gemini•San Francisco, NY

15d•Hybrid

About The Position

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 Manager, Data Engineering The Data Engineering Team owns the ingestion and transformation of data from production databases, streams, and external data sources into our data warehouse. As the Senior Manager of Data Engineering, you will lead and scale a team of data engineers, owning execution, roadmap, architectural direction, and cross-functional alignment of data infrastructure, pipelines, and platforms. You will partner with analytics, ML, product, operations, finance, and security teams to ensure data is reliable, accessible, timely, and enables the company’s strategic goals. You will translate business strategy into data initiatives, ensure best practices, and foster the growth, cohesion, and technical maturity of the data engineering organization. This role is required to be in person twice a week at either our San Francisco, CA or New York City, NY office.

Requirements

10+ years of professional experience in data engineering, software engineering, infrastructure, or related roles
3+ years of people management or leadership experience
Deep technical expertise in building large-scale data systems, including ingestion, transformation, and serving layers
Hands-on experience with data engineering tools and frameworks
Broad experience with data modeling (dimensional, canonical, normalization), schema evolution, and query performance optimization
Experience designing or evolving data platforms, shared infrastructure, or internal tooling
Strong understanding of software engineering best practices (modularity, testability, code reviews, CI/CD)
Demonstrated track record of architecting for reliability, scalability, fault tolerance, observability, and maintainability
Experience with metadata management, data governance, lineage, data catalogs
Solid cross-functional communication skills, ability to influence at senior levels
Proven ability to hire, grow, and retain engineering talent

Nice To Haves

Experience with crypto, financial services, trading, markets, or exchange systems
Experience with blockchain, crypto, Web3 data — e.g. blocks, transactions, contract calls, token transfers, UTXO/account models, on-chain indexing, chain APIs, etc.
Experience with infrastructure as code, containerization, and CI/CD pipelines
Hands-on experience managing and optimizing Databricks on AWS
Experience in financial services, markets, trading or fintech, or with regulated data environments
Experience managing hybrid, distributed or remote teams

Responsibilities

Own the team roadmap: prioritize, plan, and deliver initiatives that span multiple data domains (ingest, transformation, serving, observability)
Supervise and mentor direct reports (senior, staff, & principal data engineers), and indirectly, other engineers
Guide, review, and approve major architectural decisions, ensure alignment with cross-team standards and long-term scalability
Drive cross-functional coordination: interface with product, analytics, ML, operations, compliance, and infrastructure to ensure data initiatives deliver value
Manage capacity, resource planning, recruiting, and career development for the data engineering team
Set and monitor key metrics (e.g. pipeline reliability, latency, throughput, data quality SLAs)
Lead incident retrospectives, root cause analysis, and continuous improvement in data operations
Define and evolve the data engineering vision and architecture (batch, streaming, real-time, data modeling, metadata, lineage)
Decide technology direction: evaluate new tools, frameworks, and tradeoffs (e.g. selecting streaming engine, orchestration tool, catalog)
Enforce best practices around data quality, observability, lineage, governance, security, and compliance
Drive adoption of shared infrastructure, reusable patterns, and data platform components to reduce duplicate efforts
Ensure robustness, resilience, and monitoring of critical data systems, with appropriate alerting, failing gracefully, rollback procedures
Lead or guide on-call / support rotations and escalation protocols for production data systems
Oversee data validation, anomaly detection, schema changes, backward compatibility, and release procedures
Ensure compliance with regulatory and security requirements (e.g. PII, encryption, audit logging, access controls)
Present data engineering strategy, progress, tradeoffs, and risks clearly to executives and non-technical stakeholders
Translate business / product requirements into data requirements, schemas, SLAs, and deliverables
Act as the liaison for data engineering across teams, ensuring alignment, avoiding duplication, and managing dependencies