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Datasite develops secure software for managing complex financial transactions, including mergers and acquisitions, restructuring, and capital raising. Its data room and transaction-management platform helps buy-side and sell-side teams organize due diligence, coordinate deal activity, and collaborate securely throughout the deal lifecycle. The company serves investment banks, corporate development groups, private equity firms, and law firms, combining data-room technology with AI and machine-learning capabilities to support productivity, security, and regulatory compliance across international transactions.
Engineering Manager, Company Data
Lead engineering and people management for a proprietary company-data platform. Drive data quality, AI-enabled automation, and scalable systems supporting M&A workflows.
United States
California
New York$141,000 – $248,000 / year
HybridFull-timeMiddleSeniorEnglish RequiredEngineering ManagerDescription
- Lead a data product area while owning engineering delivery and people management
- Oversee data and product delivery across coverage, freshness, quality, performance, and commercial outcomes
- Support engineers through career development, growth planning, and promotion processes
- Build team practices for planning, execution, quality assurance, data validation, and operational excellence
- Represent Engineering in cross-functional planning with Product, Data, AI, Design, Data Operations, and customer-facing teams
- Manage platform debt, schema changes, pipeline reliability, and operational risk
- Convert product, data, and technical strategy into execution plans, actionable work, and measurable results
- Shape architecture, remove team blockers, and contribute to high-impact data initiatives
- Build and scale a company-data platform spanning ingestion, enrichment, normalization, entity resolution, quality controls, and serving layers
- Improve the freshness, coverage, and accuracy of company profiles, financial data, transactions, ownership, firmographics, and market intelligence
- Automate data workflows and transform inconsistent sources into reliable product experiences
- Create platform capabilities that make data more searchable, actionable, and defensible across M&A business-development workflows
- Improve observability, evaluation, and incident response across data systems
Requirements
- Experience managing engineering teams that build data-intensive products, proprietary datasets, or large-scale data platforms
- Strong working knowledge of Python, SQL, and backend services in data-heavy environments
- Ability to provide technical direction on full-stack and API work when data capabilities reach the product
- Experience designing and operating ETL or ELT pipelines, data-quality systems, and queue-based, event-driven, or asynchronous workflows
- Solid understanding of data modeling, entity resolution, schema evolution, reliability, observability, architectural patterns, and engineering trade-offs
- Experience with AI-assisted and agentic coding workflows
- Ability to establish acceptance criteria, evaluations, tests, metrics, and code or design review practices
- Ability to link technical and data investments to customer value, product strategy, and commercial results
- Familiarity with Databricks, Spark, distributed processing, Kafka, Pulsar, Elasticsearch or OpenSearch, graph databases, search infrastructure, LLM-assisted data workflows, entity resolution, deduplication, taxonomy or ontology design, knowledge-graph data models, cloud-native architectures, and scalable backend services is helpful
- Demonstrated judgment, ownership, and collaborative working style
- Ability to anticipate cross-team dependencies, balance technical and data risk, forecast capacity, negotiate scope, plan across quarters, communicate with technical and non-technical stakeholders, guide architectural evolution, and improve processes and data-quality feedback loops
Benefits
- Medical, dental, and vision health insurance
- Retirement savings plan
- Paid time off
- Potential eligibility for bonuses, commissions, or overtime where applicable
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