Principal, Data Analyst - Engineering - US - Freelancer | Digital Products Team

DEPT®

1d•Remote

About The Position

We are pioneers at heart. What this means, is that we are always leaning forward, thinking of what we can create tomorrow that does not exist today. We were born digital and we are a new model of agency, with a deep skillset in tech and marketing. That’s why we hire curious, self-driven, talented people who never stop innovating. Our culture is big enough to cope and small enough to care. Meaning, that with people across 20+ countries, we’re big enough to provide you with the best tools, global opportunities, and benefits that help you thrive. While acting small by investing in you, your growth, your team, and giving you the autonomy to solve our clients' problems, no matter where you are in the world. Please note this is a Full time contract opportunity It will be 3 months, and is expected to extend in 3 month increments into 2027. Candidates must be willing to work 4 hours daily with EMEA team for meetings, as most of the team is in Europe. The data strategist will be a key contributor responsible for defining and executing the vision for how our client's analytics transforms, governs, and delivers its core data assets. This role involves driving the strategy, innovation, and operational excellence of client data product portfolio, collaborating with cross-functional teams, and ensuring the highest standards of data quality, scalability, and value delivery. The successful candidate will bring deep expertise in data strategy, set best practices for data excellence, and champion a culture of data-centric product development that directly supports client's mission to decode risk and unlock opportunity.

Requirements

8+ years of experience in Data Strategy, or related roles, with demonstrated expertise in defining and executing a multi-year product strategy for complex, large-scale data assets, preferably in a B2B or Enterprise environment.
Strong understanding of modern product management and Agile methodologies (Scrum/Kanban) at a portfolio level.
Deep expertise in understanding data content, data models, and data structures, with the ability to oversee and guide the design and optimization of data structures across the entire product portfolio to ensure scalability, usability, and alignment with business needs.
Solid technical acumen, with experience overseeing data processing pipelines, data warehousing technologies (e.g., Snowflake, Redshift), and data governance tools.
Exceptional written and verbal communication skills, with the ability to articulate complex technical concepts to executive-level, non-technical audiences.
Bachelor’s degree in a technical or quantitative field (e.g., Computer Science, Engineering, Finance, or Data Science).

Nice To Haves

Deep expertise in the Finance, Risk, or Regulatory sectors, with an understanding of the critical importance of data quality and timeliness in these domains.
An MBA or Master’s degree is a plus.

Responsibilities

Data Strategy & Planning: Define and execute long-term strategic initiatives and a multi-year roadmap for data product portfolio, ensuring alignment with business goals and technology architecture.
Innovation & AI: Identify and evaluate emerging technologies, including AI/ML capabilities, to enhance data assets, streamline delivery, and unlock new value for both internal and external stakeholders.
Data Discovery: Partner with stakeholders to discover, evaluate, and onboard new data sources to expand the portfolio’s value and relevance for business needs.
Data Product Requirements: Lead the creation and ownership of detailed data briefs and technical requirements to define and guide data querying, transformation, enrichment, and delivery processes. Ensure all requirements are aligned with business objectives, application needs, and end-user expectations, enabling the development of scalable, high-quality data products that deliver measurable value.
Data-Driven Decision-Making: Collaborate with business units to identify key performance indicators (KPIs) and develop data-driven frameworks for decision-making.
Delivery Assurance: Collaborate with product, engineering, architecture, and application teams to ensure successful execution of data initiatives, meeting performance, scalability, and quality standards.
Risk Management: Proactively identify and mitigate risks and dependencies across data product initiatives, ensuring transparency and clear escalation paths to senior leadership.
Data Governance: Advocate for and enforce rigorous standards of data quality, metadata management, data lineage, and compliance across the portfolio.
Scalability & Architecture: Partner with Data Engineering and Data Architecture teams to ensure the data infrastructure is secure, scalable, and aligned with future business needs.
Automation & Monitoring: Drive the adoption of automation and monitoring tools to enhance operational efficiency, reduce failure points, and optimize the end-to-end data lifecycle.
Executive Communication: Provide clear, concise updates on data product strategy, progress, and risks to senior and executive stakeholders.
Cross-Functional Partnership: Collaborate closely with Product Strategy, Product, Engineering, QA, and Business teams to ensure seamless integration of data assets into delivery platforms.