Data Partnerships & Operations Manager

M Science; New York (NY); On-site; USD 110,000 - 130,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

401(k)

Paid time off

Job summary

M Science in New York is seeking a Data Partnerships & Vendor Management professional to own vendor relationships, coordinate data ingestion and testing, and oversee data quality for research teams.

The role requires cross-functional collaboration with engineering, analytics, and operations to maintain SLAs, resolve issues, and guide data governance. Strong communication and PM skills are essential for success in this highly collaborative environment.

Qualifications

• 3–5+ years of experience in project management, data partnerships, vendor management, or data operations, ideally in differentiated data, financial services, or investment research.
• Working knowledge of data pipelines and ingestion concepts (APIs, S3/cloud storage delivery, batch vs. incremental loads, schemas, data dictionaries).
• Familiarity with Databricks, Airflow, or similar data platforms; SQL proficiency strongly preferred.
• Experience monitoring vendor SLAs and data quality and communicating impact to business stakeholders.
• Strong project management skills and the ability to run multiple vendor relationships and data tests in parallel.
• Excellent written and verbal communication; comfortable translating between technical teams, vendors, and research analysts.
• Highly organized, detail-oriented, and proactive about documentation and process.

Responsibilities

• Data Partnerships and Vendor Management: Serve as the primary day-to-day contact for data vendors, manage escalations, onboarding and credentialing, and reconcile statements against usage and invoicing.
• Data Ingestion Coordination: Liaise between ingestion team and vendors; prioritize requests; coordinate trial datasets and schema consistency; monitor missing files and QC alerts.
• Data Testing and Evaluation: Lead project and timeline management for data tests and vendor trials; coordinate testing with stakeholders and maintain documentation templates.
• Dataset Onboarding and Integration: Track data use progress; coordinate client-facing communications; own data lineage for production.
• SLA Monitoring, Reporting, and Data Quality: Own SLA monitoring and monthly reporting; manage disruption logging and tooling improvements; maintain alert channels.
• Cross-Functional Communication: Act as communications layer across data partnerships, testing, research, and ingestion; field questions and publish updates.
• Strategic Initiatives: Drive initiatives for ingestion standardization, anomaly detection extension, and self-service ingestion capabilities.

Skills

Vendor management

Data partnerships

Project management

Data operations

SQL

Databricks

Airflow

Communication

Tools

Databricks

Airflow

Job description

The Data Partnerships & Operations Manager will own the relationship between external data vendors, internal data ingestion team, and research analysts. This person sits at the intersection of new and existing datasets, including managing vendor relationships, coordinating ingestion and testing, tracking data quality and SLAs, as well as ensuring research teams are kept apprised on data status.

The role is highly cross-functional and will require technical understanding of the ingestion process and technologies to troubleshoot at a high level and determine issue sources, as well as communication with vendors, engineers, and analysts.

Data Partnerships and Vendor Management

• Serve as the primary day-to-day point of contact for a portfolio of data vendors, including account management, escalations, and ongoing communication.
• Coordinate technical transitions with vendors, such as new endpoints, version migrations, API requirements, and delivery method changes.
• Manage vendor platform access and admin accounts, including onboarding internal users and coordinating credentials and accessing key rotation with the engineering team.
• Manage vendor restatements end to end, including notifying research, driving decisions, and coordinating with ingestion.
• Reconcile vendor statements against actual usage, monitor usage against contracted limits to flag overages early, and verify invoice accuracy before approval.
• Maintain vendor-specific knowledge, including key contacts, escalation paths, open issues, known caveats, and dataset quirks.
• Support renewals by confirming how vendor definitions and methodology changes will affect coverage and existing research pipelines.

Data Ingestion Coordination

• Act as the liaison between the ingestion team and vendors.
• Prioritize incoming ingestion requests and provide ingestion context (full refresh vs. append, data dictionaries, table naming, and schema expectations).
• Coordinate the handling of trial datasets, including standardized naming and schema consistency to expedite research team testing.
• Monitor missing files and ingestion QC alerts and follow up with vendors on late or failed deliveries.
• Improve stakeholder ingestion request forms and workflows, coordinating new features with engineering.
• Participate in sprint planning with engineering leads, flagging upcoming changes from data partners that affect ingestion priorities.
• Communicate disruptions, outages, delays, and new table versions to impacted stakeholders.

Data Testing and Evaluation

• Lead project and timeline management for high-priority data tests and vendor trials.
• Coordinate with stakeholders during testing, including ticker and KPI testing, and collate feedback.
• Own vendor communications throughout the testing process.
• Maintain and update data testing templates (such as test plans, evaluation checklists, and feedback forms) and supporting documentation so research teams can perform consistent dataset evaluations.
• Build and maintain working knowledge of existing datasets, including data types, key stakeholders, usage guidance, and limitations.

Dataset Onboarding and Integration

• In conjunction with research operations, track data use progress.
• Coordinate client-facing communications strategy.
• Own data lineage process for production.

SLA Monitoring, Reporting, and Data Quality

• Own SLA monitoring and monthly reporting, including calculation methodology, partner-specific reporting requirements, and vendor notifications.
• Own vendor disruption logging, including quantifying product impact, and maintain a process for others to submit issues.
• Coordinate technical improvements to monitoring and reporting tooling with engineering.
• Maintain dataset alert distribution lists and communication channels for data testing and active datasets, including adding new research hires to the relevant lists and channels.

Cross-Functional Communication

• Act as the communications layer across data partnerships, data testing, research, and ingestion.
• Field internal questions on best practices, dataset status, outages, and disruptions.
• Communicate new data partnership information to the research department.
• Attend data science standups to stay current on initiatives and team capacity.

Strategic Initiatives

This role will drive current initiatives to completion and help identify and set the strategic direction for future data partnerships operational priorities.

• Help drive ingestion standardization, including harmonizing timestamps, maintaining raw data catalogs, retiring redundant tables, and adding table metadata.
• Support extension of anomaly detection on ingested data, including root cause analysis.
• Develop self-service ingestion capabilities that automate the addition of new API-based data vendor requests, eliminating manual configuration and reducing operational overhead.
• Contribute to a vendor knowledge library and to vendor cost and usage visibility.
• Support AI-enabled access to vendor data, including vendor MCP access, AI-use agreements, and a reusable data testing skill.

Qualifications

• 3–5+ years of experience in project management, data partnerships, vendor management, or data operations, ideally in differentiated data, financial services, or investment research.
• Working knowledge of data pipelines and ingestion concepts (APIs, S3/cloud storage delivery, batch vs. incremental loads, schemas, data dictionaries).
• Familiarity with Databricks, Airflow, or similar data platforms; SQL proficiency strongly preferred.
• Experience monitoring vendor SLAs and data quality and communicating impact to business stakeholders.
• Strong project management skills and the ability to run multiple vendor relationships and data tests in parallel.
• Excellent written and verbal communication; comfortable translating between technical teams, vendors, and research analysts.
• Highly organized, detail-oriented, and proactive about documentation and process.

Nice to Have

• Experience with transaction, app, web-scraped, or other differentiated datasets used in equity research.
• Exposure to contract terms relevant to data licensing (SLAs, use rights, AI-use provisions).
• Python skills for basic data exploration and QC.
• Interest in AI tooling for data access and research workflows.

Compensation: $110,000 - $130,000 USD/Annual

The salary offered will take into consideration an individual’s experience level and qualifications. In addition to salary, M Science offers, for eligible employees, an annual discretionary incentive bonus and competitive employee benefits, including medical, dental, and vision coverage; a 401(k); life, accident, and disability insurance; and wellness programs, including discounted and flexible gym memberships; and a robust employee discount program. M Science also offers paid time off packages that include planned time off (vacation), unplanned time off (sick leave), paid holidays and paid parental leave.