Senior Manager, Data Engineering

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.

Responsibilities:

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

Minimum Qualifications:

1. 10+ years of professional experience in data engineering, software engineering, infrastructure, or related roles
2. 3+ years of people management or leadership experience
3. Deep technical expertise in building large-scale data systems, including ingestion, transformation, and serving layers
4. Hands-on experience with data engineering tools and frameworks
5. Broad experience with data modeling (dimensional, canonical, normalization), schema evolution, and query performance optimization
6. Experience designing or evolving data platforms, shared infrastructure, or internal tooling
7. Strong understanding of software engineering best practices (modularity, testability, code reviews, CI/CD)
8. Demonstrated track record of architecting for reliability, scalability, fault tolerance, observability, and maintainability
9. Experience with metadata management, data governance, lineage, data catalogs
10. Solid cross-functional communication skills, ability to influence at senior levels
11. Proven ability to hire, grow, and retain engineering talent

Preferred Qualifications:

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

It Pays to Work Here

The compensation & benefits package for this role includes:

1. Competitive starting salary
2. A discretionary annual bonus
3. Long-term incentive in the form of a new hire equity grant
4. Comprehensive health plans
5. 401K with company matching
6. Paid Parental Leave
7. Flexible time off

Salary Range: The base salary range for this role is between $192,500 - $275,000 in the State of New York, the State of California and the State of Washington. This range is not inclusive of our discretionary bonus or equity package. When determining a candidate’s compensation, we consider a number of factors including skillset, experience, job scope, and current market data.