Key Responsibilities and Required Skills for Data Strategist

🎯 Role Definition

A Data Strategist defines and drives the organization's data strategy to enable data-driven decision making and measurable business outcomes. This role translates business priorities into an actionable data roadmap, establishes governance, stewardship and architecture principles, partners with analytics, engineering, and product teams to operationalize data products, and measures ROI on data initiatives. The Data Strategist is a cross-functional leader who combines deep business acumen with technical fluency in cloud data platforms, BI, metadata management, and data governance frameworks.

📈 Career Progression

Typical Career Path

Entry Point From:

Senior Business Analyst or Data Analyst with strategy exposure
BI Manager, Analytics Manager or Product Manager (Data-focused)
Data Engineer, Management Consultant specializing in analytics and digital transformation

Advancement To:

Head of Data Strategy / Director of Data Strategy
VP of Data, Analytics or Chief Data Officer (CDO)
Head of Data Product Management or Business Intelligence

Lateral Moves:

Data Product Manager
Director of Analytics / Business Intelligence
Head of Data Governance or Master Data Management (MDM)

Core Responsibilities

Primary Functions

Develop and maintain a multi-year enterprise data strategy and roadmap that aligns with company goals, monetization objectives, and product priorities; define measurable success metrics and KPIs for all major data initiatives.
Lead cross-functional stakeholder engagement (C-suite, product, marketing, finance, engineering) to translate business use cases into prioritized data initiatives, business cases, and value estimates (ROI, cost-benefit analysis).
Design and operationalize data governance frameworks including policies for data ownership, stewardship, quality rules, lineage, metadata management, and role-based access to ensure compliance and trust in enterprise data.
Define and socialize data architecture principles and target-state reference architecture (including data mesh, lakehouse, or centralized warehouse patterns) to guide engineering and vendor decisions.
Own the data product lifecycle: ideation, discovery, product requirements, prioritization, launch criteria, adoption tracking, and sunset decisions for analytics and data products.
Partner with data engineering and platform teams to specify data contracts, ingestion patterns, ETL/ELT design, schema requirements, and SLAs that meet business use cases and latency objectives.
Establish and run a data governance council or steering committee, facilitating decision-making on standard definitions (business glossaries), master data domains, and cross-functional escalation of data issues.
Define measurement frameworks and dashboards for revenue, growth, retention, and operational efficiency — and ensure instrumentation (event tracking, data capture) is complete and validated for analytics and experimentation.
Drive data quality program implementation: define SLOs, automated monitoring, alerting, remediation paths, and assign stewardship responsibilities across business domains.
Evaluate, select, and manage third-party data vendors, SaaS analytics tools, BI platforms (Tableau, Power BI, Looker), and cloud data services (Snowflake, Databricks, BigQuery, Redshift) to accelerate delivery while managing TCO.
Lead cross-functional initiatives to operationalize machine learning models and advanced analytics into production processes with clearly defined success criteria and monitoring.
Create and maintain a data catalog and metadata management practices to accelerate discovery, reduce duplication, and improve data literacy across the organization.
Build a prioritized backlog of analytics and data product work with product managers and engineering leads; translate strategic priorities into epics, user stories, and acceptance criteria.
Author data policies and privacy compliance controls in partnership with Legal and Security (GDPR, CCPA, HIPAA where applicable), and operationalize data handling procedures for sensitive and regulated data.
Produce regular executive reporting and storytelling: translate data program progress, risks, and business impact into one-page summaries and presentations for leadership.
Conduct stakeholder readiness and adoption programs, including data literacy training, onboarding playbooks for new data products, and KPI-retrospective sessions to ensure sustained value realization.
Drive experimentation and A/B testing strategies in partnership with product and growth teams; ensure test design, telemetry, analysis, and attribution meet statistical rigor and business interpretation standards.
Define master data management (MDM) approaches and integration strategies to create single sources of truth for customer, product, and financial dimensions.
Manage budget and resource allocation for strategic data investments; prioritize spend across tooling, engineering, vendor services, and talent acquisition based on expected business impact.
Mentor and develop a high-performing team of data strategists, analytics translators, and domain stewards; set performance goals, career development plans, and hiring profiles.
Conduct regular readiness reviews and risk assessments for large data programs, including dependency mapping, compliance audits, and contingency planning.
Translate complex technical concepts for non-technical stakeholders and ensure alignment on timelines, trade-offs, and business value; act as the primary internal evangelist for data-driven decision making.
Partner with engineering to create scalable instrumentation, event taxonomies, and telemetry best practices to ensure analytics accuracy and repeatability.
Measure and report adoption, ROI, and business outcomes of data products and governance initiatives; iterate the roadmap based on empirical evidence and stakeholder feedback.
Coordinate with finance and procurement to evaluate vendor contracts, negotiate SLAs, and manage licensing to optimize costs and scalability.
Lead cross-functional workshops (discovery, data modeling, use-case mapping, and prioritization) to accelerate consensus and reduce rework across data and business teams.
Oversee the onboarding of newly acquired or merged data assets: harmonize schemas, reconcile master data, and integrate reporting to achieve fast, consolidated insights.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.
Maintain documentation of data standards, playbooks, and runbooks to ensure operational continuity.
Facilitate cross-team workshops to identify reuse opportunities and reduce redundant analytics efforts.
Assist in vendor due diligence and proof-of-concept evaluations for analytics and data platform solutions.
Provide periodic training sessions and office hours for analytics consumers and power users.
Coordinate with security teams to enforce access controls and data sharing agreements.
Assist product teams in defining event schemas and instrumentation standards to support downstream analytics.

Required Skills & Competencies

Hard Skills (Technical)

Enterprise data strategy development and roadmap planning.
Data governance frameworks (DAMA, DCAM) and stewardship implementation.
Strong SQL expertise for data modeling, validation, and analysis (advanced query tuning and optimization).
Proficiency with cloud data platforms and services (Snowflake, BigQuery, Redshift, Databricks, AWS/GCP/Azure).
Experience with BI and dashboarding tools (Tableau, Power BI, Looker/LookML, Qlik).
Familiarity with data engineering concepts: ETL/ELT, streaming ingestion, CDC, data pipelines and orchestration (Airflow, dbt, Kafka).
Metadata management and data catalog tools (Alation, Collibra, Amundsen, Glue Data Catalog).
Knowledge of master data management (MDM) patterns and implementation experience.
Working knowledge of analytics and statistical techniques (A/B testing, regression, cohort analysis) and basic Python/R for prototyping.
Experience operationalizing ML models and working with ML engineering or MLOps teams.
Data privacy, security and compliance: GDPR, CCPA, HIPAA fundamentals and practical controls.
Vendor evaluation and contract negotiation experience for data and analytics software.
ROI and business case development for data investments, including TCO modeling.

Soft Skills

Strategic thinking and ability to align data initiatives to business objectives.
Excellent written and verbal communication for executive-level storytelling and stakeholder influence.
Strong interpersonal skills and experience leading cross-functional teams without direct authority.
Problem solving and structured analytical approach to dissect complex business problems.
Change management and coaching to increase data literacy across the organization.
Prioritization and resource management to balance quick wins with long-term capabilities.
Facilitation skills for workshops, governance councils, and stakeholder alignment sessions.
Curiosity and continuous learning mindset to evaluate emerging data technologies and best practices.
Resilience and adaptability in ambiguous, fast-paced environments.
Mentorship and talent development for building high-performing data teams.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Computer Science, Data Science, Statistics, Business Analytics, Information Systems, Economics, Mathematics, or related field.

Preferred Education:

Master's degree or MBA with focus on analytics, strategy, or information systems preferred.
Professional certifications in data governance, cloud platforms (AWS/GCP/Azure), or analytics (e.g., SnowPro, Google Cloud Certified, Tableau Certified).

Relevant Fields of Study:

Computer Science / Software Engineering
Data Science / Statistics / Mathematics
Business Analytics / Economics
Information Systems / Management Information Systems
Operations Research / Industrial Engineering

Experience Requirements

Typical Experience Range:

5–12+ years of progressive experience across analytics, data strategy, data governance or related roles.

Preferred:

7+ years leading or owning data strategy, governance, or analytics programs in mid-to-large enterprise settings.
Demonstrable experience working with cloud data technologies (Snowflake, Databricks, BigQuery), BI tools, and cross-functional product/engineering teams.
Experience presenting to and influencing senior leadership (C-suite), managing budgets, and delivering measurable business impact from data initiatives.