Senior Level Data Analytics Associate Jobs
Senior level data analytics associate jobs place experienced professionals in charge of analytical strategy, end-to-end project ownership, and guiding the teams and stakeholders who act on data-driven insights. Openings are concentrated across Technology & Software, Biotechnology & Pharmaceuticals, and Insurance, with a mix of on-site, remote, and hybrid settings, and employers like Capital One, Revolution Medicines, and Revolution Medicines hiring at this level now.
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INTRODUCTION
The Talent Acquisition department hires qualified candidates to fill positions which contribute to the overall strategic success of Howard University. Hiring staff “for fit” makes significant contributions to Howard University’s overall mission. At Howard University, we prioritize well-being and professional growth.
Here Is What We Offer
- Health & Wellness: Comprehensive medical, dental, and vision insurance, plus mental health support
- Work-Life Balance: PTO, paid holidays, flexible work arrangements
- Financial Wellness: Competitive salary, 403(b) with company match
- Professional Development: Ongoing training, tuition reimbursement, and career advancement paths
- Additional Perks: Wellness programs, commuter benefits, and a vibrant company culture
Join Howard University and thrive with us!
Howard University is building a new data shop and we are looking for a Chief Data and Analytics Officer. The Chief Data and Analytics Officer (CDAO) is an executive business leadership role focused on leveraging data, analytics and AI, to drive business value, foster a data-driven culture, and ensure the effective governance and utilization of data assets and ecosystem across the organization. This includes the creation and management of data and analytics strategy and operating model. The CDAO is responsible for establishing, leading and operating the D&A function; building trust and managing data; evolving technology capabilities and developing talent and D&A culture.
THE CDAO
- Defines data, analytics and AI strategy, including vision, drivers and outcomes. Leads the creation (and assure the ongoing relevance) of the organization’s D&A strategy in collaboration with the CEO, business domain leaders, CIO and other relevant stakeholders.
- Institutes an operating model for data, analytics and AI that is consistent with the capabilities and competencies required to execute the strategy. The operating model includes the ecosystem, architectures and delivery model.
- Builds partnerships with executive leadership and board members to ensure that data is being managed as a business asset, data is AI-ready, and by tracking and measuring the value derived from those data assets. Communicates the tangible business value generated from data, analytics and AI initiatives to stakeholders and executives.
- Maintains authority and accountability for the data assets, and of the analytics used for decision making, and AI solutions that automate decisions and augment human performance. Creates and oversees a centralized data management/data engineering service for sharing certified data products to ensure quality, traceability, timeliness, usability and cost-effectiveness.
- Oversees delivery models, methods and practices for creation of data, analytics and AI products and to ensure consistent application and use of data and analytics solutions and services, including data science.
- Evolves technology capabilities for the D&A platform, in collaboration with the chief information officer (CIO) to align D&A initiatives with IT infrastructure and policies, and drive technology innovation across the organization.
- Establishes and maintains trust in AI-ready data assets by instituting governance mechanisms for data including fostering data stewardship across business data domains. Includes other leaders that share responsibility for data, such as security, privacy, risk and compliance leaders.
- Understands regulatory requirements, relevant data protection laws and regulations, such as GDPR, EU AI Act and CCPA, and industry-specific standards. Ensures the organization's data practices are compliant, in collaboration with legal and compliance.
- Oversees the ethical and responsible use of data and algorithms used for artificial intelligence, analytics and automated decision making. Establishes guidelines and practices that prevent misuse of data and protect individual privacy, beyond regulatory and compliance requirements.
- Owns the development of a data-driven culture, related competencies, behaviors, and data and AI literacy across the enterprise. Leads these transformation efforts by developing D&A talent and maturing the D&A capability of the organization.
MANAGEMENT AND OPERATIONS
- Develop, manage, allocate and govern the annual budget for the office of the CDAO.
- Organize and lead a data and analytics center of excellence (aka the office of the CDAO or D&A hub) and constantly improve the organization’s capacity to develop insights with advanced analytics.
- Define members’ responsibilities and accountabilities. Define job roles, recruit candidates, and then manage (directly or indirectly) a team of data and analytics leaders and senior information management professionals in different regions or business units across a complex, international group enterprise.
- Oversee development, publishing and maintenance of the organization’s data and analytics architecture and platforms, as well as a roadmap for its future development, ensuring that it matches and supports business needs.
- Partner with the CIO to scan the horizon for emerging management tools, skills, techniques and technologies. Work together to ensure their timely introduction for competitive advantage and to minimize duplication.
KNOWLEDGE/SKILLS
- The CDAO must be an executive leader with an understanding of business objectives and goals; industry domain expertise; and knowledge of D&A concepts, methods and techniques.
- The CDAO must have exceptional interpersonal and leadership skills with a focus on stakeholder engagement and communication.
- The CDAO will work as a peer with other C-level executives and the President to facilitate their data-driven vision, strategy and operating models.
- The CDAO needs to have an excellent understanding of and ability to drive excellence in the business process and associated data. The CDAO needs to be highly proficient in these transferable leadership skills:
- Demonstrated leadership: Proven track record of leading complex, multidisciplinary talent teams in new endeavors and delivering solutions. Ability to balance team and individual responsibilities, build teams and consensus, get things done through others not directly under their supervision, and work ethically and with integrity.
- Excellent business acumen and interpersonal skills: Able to work across business lines at a senior level to influence and effect change to achieve common goals.
- Communication and storytelling: Creating consistent value-oriented storylines for better understanding and execution of the D&A strategy. Ability to effectively drive business, culture and technology change in a dynamic and complex operating environment (e.g., conveying information to diverse audiences in a way that is easily understood and actionable). Comfort with public speaking and the ability to creatively and concisely express ideas to business and technical audiences.
- Influencing and emotional intelligence: For example, by asserting ideas and persuading others to gain support across an organization or to adopt new behaviors. Ability to explain digital concepts and technologies to business leaders, and business concepts to technologists. Can “sell” ideas and processes internally at all levels, including the board and investors.
- Facilitation: For example, hosting sessions to elicit ideas from others, understanding their issues and encouraging group participation.
- Creative and critical thinking, and outstanding analytical and problem-solving abilities: Familiarity with business information generation and analysis methods (for example, framing new concepts that spur use case ideation for business participants and brainstorming with business users about future product and services).
- Teamwork: For example, working with business domain teams as well as D&A teams and IT staff.
- Collaboration: For example, fostering group problem solving and solution creation with business and technical team members.
- Relationship management: For example, creating relationships and quickly building trust with internal and external stakeholders.
Job Requirements
Education
Preferably a bachelor’s or master’s degree in business administration; science technology, engineering and mathematics (STEM); computer science; data science; information systems or related field; or equivalent work experience. Academic qualification or professional training and experience in legal and regulatory areas are also desirable.
Experience
- 10-15 years of business experience, ideally in business management, legal, financial or information or IT management recently at or near the executive level.
- Broad business experience internally and within the vertical industry is desired.
- 5 or more years of progressive leadership experience in leading cross-functional teams, multidisciplinary environments and enterprise-wide data and analytics programs, operating and influencing effectively across the organization and within complex contexts.
- Experience in integrating complex, cross-corporate processes and information strategies, and/or designing strategic metrics and scorecards.
- Strategy and management consulting experience desirable.
COMPLIANCE SALARY RANGE DISCLOSURE
Compensation Range: $180,000 - $190,000
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Top Industries Hiring
- Technology & Software
- Biotechnology & Pharmaceuticals
- Insurance
- Retail
- Banking & Financial Services
Senior Level Data Analytics Associate Jobs: Frequently Asked Questions
How do I get a senior level data analytics associate job?
Employers hiring at the senior level look for candidates who can own complex analytical workstreams from framing the business question through delivering a recommendation, not just execute assigned tasks. Strong candidates demonstrate proficiency with advanced tools such as SQL, Python, or Tableau, a history of influencing decisions at the stakeholder level, and experience mentoring junior analysts. Tailoring your resume to show scope, impact, and cross-functional leadership gives you a clear edge.
Which companies hire senior level data analytics associates?
Companies hiring senior level data analytics associates right now include Capital One, Revolution Medicines, and Revolution Medicines, based on current listings on Migrate Mate as of November 2026. Hiring at this level tends to come from large enterprises and growth-stage organizations that run dedicated analytics functions and need experienced professionals to lead projects and drive data strategy.
Are there remote senior level data analytics associate jobs?
Yes, though availability varies by employer and industry. About 43% of senior level data analytics associate openings are remote or hybrid as of November 2026, reflecting the analytical nature of the work and the widespread adoption of cloud-based data tools. On-site roles do exist, particularly in industries where data governance or regulated data environments require closer collaboration.
What makes a data analytics associate role senior level?
Senior level roles are defined by ownership and scope rather than task execution. A senior data analytics associate is expected to frame analytical problems independently, determine the right methodology, communicate findings to leadership, and often mentor or coordinate junior team members. The technical depth required is higher, and the expectation is that you contribute to how the analytics function operates, not just what it produces.
Which industries hire the most senior level data analytics associates?
Senior level data analytics associate roles concentrate in Technology & Software, Biotechnology & Pharmaceuticals, and Insurance, based on current listings on Migrate Mate as of November 2026. These sectors tend to generate large volumes of structured and unstructured data, creating sustained demand for experienced analysts who can translate complex datasets into decisions that affect operations, product, or customer strategy.