Torchora
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Key Responsibilities and Required Skills for Head of Data

💰 $200,000 - $350,000+ (Competitive and commensurate with experience)

Data & AnalyticsLeadershipTechnologyManagementData Science

🎯 Role Definition

Are you a strategic data leader passionate about transforming raw data into a core business asset? As our Head of Data, you will be the definitive owner of our organization's data ecosystem. Reporting directly to the Chief Technology Officer (CTO), this pivotal role involves crafting and executing a comprehensive data strategy that aligns with our company's ambitious goals. You will be responsible for leading our data science, data analytics, and data engineering functions, building the infrastructure, teams, and processes necessary to drive innovation, optimize operations, and unlock new revenue opportunities. This is a unique opportunity to build a data-first culture from the ground up and make a significant impact on our company's trajectory.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Director of Data Science / Analytics
  • Principal Data Architect
  • Senior Manager, Data Engineering
  • Head of Business Intelligence

Advancement To:

  • Chief Data Officer (CDO)
  • Chief Data & Analytics Officer (CDAO)
  • VP of Technology / Engineering
  • Chief Information Officer (CIO)

Lateral Moves:

  • Head of Product
  • Head of Engineering
  • VP of Strategy & Operations

Core Responsibilities

Primary Functions

  • Develop and execute a comprehensive, long-term data vision and strategy that aligns with and drives key business objectives and outcomes.
  • Lead, mentor, and grow a multi-disciplinary team of data scientists, data engineers, and data analysts, fostering a culture of excellence, collaboration, and continuous professional development.
  • Architect, build, and maintain a modern, scalable, and reliable data infrastructure, including data warehousing, data lakes/lakehouses, and ETL/ELT pipelines.
  • Establish and enforce a robust data governance framework, ensuring data quality, integrity, privacy, security, and compliance with regulations like GDPR and CCPA.
  • Champion the democratization of data, empowering business users with self-service analytics tools, dashboards, and reporting capabilities to make informed decisions.
  • Partner with executive leadership and cross-functional stakeholders (Product, Marketing, Sales, Finance) to identify business challenges and opportunities that can be addressed with data-driven solutions.
  • Oversee the entire lifecycle of data products, from conceptualization and requirements gathering to deployment, monitoring, and iteration.
  • Drive the advanced analytics and machine learning roadmap, identifying opportunities to leverage predictive modeling, AI, and statistical analysis to create a competitive advantage.
  • Define and monitor key performance indicators (KPIs) and metrics to measure the effectiveness of the data function and the impact of data initiatives on the business.
  • Manage the departmental budget, including technology procurement, vendor relationships, and resource allocation to ensure optimal ROI on data investments.
  • Stay at the forefront of emerging trends, technologies, and best practices in the data and analytics landscape, and evaluate their potential application within the organization.
  • Implement and standardize best practices for data modeling, data warehousing, and software engineering to ensure consistency and quality across all data projects.
  • Act as the primary evangelist for a data-driven culture, promoting data literacy and analytical thinking throughout the entire organization through training and communication.
  • Oversee the selection and implementation of the enterprise data technology stack, ensuring tools and platforms are fit for purpose and scalable for future growth.
  • Translate complex data concepts and the results of analytical models into clear, compelling narratives and actionable recommendations for non-technical audiences.
  • Establish agile methodologies and streamlined workflows for the data team to ensure timely and efficient delivery of data projects and analytical requests.
  • Foster strong relationships with third-party data providers and technology partners to enhance the organization's data capabilities.
  • Develop and maintain a central data dictionary and metadata management system to provide a single source of truth for all key business metrics and data assets.
  • Lead the strategic design and implementation of A/B testing and experimentation frameworks to measure product and marketing effectiveness.
  • Ensure the ethical use of data and AI across the organization, establishing guidelines and review processes to mitigate bias and risk.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis to answer urgent business questions.
  • Contribute to the organization's broader technology strategy and long-term roadmap.
  • Collaborate with business units to translate high-level data needs into detailed engineering and analytics requirements.
  • Participate in sprint planning, retrospectives, and other agile ceremonies within the data teams.
  • Author and contribute to internal documentation, whitepapers, and presentations on data strategy and outcomes.

Required Skills & Competencies

Hard Skills (Technical)

  • Cloud Data Platforms: Deep expertise in at least one major cloud provider's data ecosystem (AWS - Redshift, S3, Glue; GCP - BigQuery, Cloud Storage; Azure - Synapse Analytics).
  • Modern Data Warehousing/Lakehouse: Hands-on experience architecting and managing modern data platforms like Snowflake, Databricks, BigQuery, or Redshift.
  • Data Transformation & Orchestration: Proficiency with modern data transformation tools (dbt) and workflow orchestration engines (e.g., Airflow, Dagster, Prefect).
  • Business Intelligence & Visualization: Mastery of BI tools like Tableau, Power BI, Looker, or Metabase for creating impactful dashboards and reports.
  • Data Modeling: Strong understanding of data modeling techniques, including dimensional modeling (Kimball) and 3NF, and their practical application.
  • SQL & Python: Expert-level proficiency in SQL and strong programming skills in Python for data analysis, pipeline development, and scripting.
  • ETL/ELT Systems: In-depth knowledge of designing, building, and maintaining robust and scalable data ingestion and integration pipelines.
  • Data Governance & Quality: Experience implementing data governance tools (e.g., Alation, Collibra) and establishing data quality monitoring frameworks.
  • Machine Learning & MLOps: Foundational understanding of machine learning concepts and the lifecycle of model development and deployment (MLOps).
  • Infrastructure as Code (IaC): Familiarity with tools like Terraform or CloudFormation for managing data infrastructure programmatically.
  • Big Data Technologies: Experience with distributed computing frameworks like Spark is highly advantageous.

Soft Skills

  • Strategic Vision: Ability to think long-term and create a clear, compelling vision for the future of data in the organization.
  • Leadership & Mentorship: Proven ability to build, inspire, and develop high-performing technical teams.
  • Executive Communication: Exceptional ability to communicate complex technical ideas to non-technical stakeholders and executive leadership.
  • Business Acumen: Strong understanding of business operations and the ability to connect data initiatives directly to business value and P&L.
  • Stakeholder Management: Skill in building relationships, influencing others, and driving alignment across different departments and seniority levels.
  • Problem-Solving: A pragmatic and analytical approach to solving complex business and technical challenges.
  • Change Management: Experience leading organizational change and successfully fostering the adoption of new technologies and processes.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's Degree in a quantitative or technical field.

Preferred Education:

  • Master’s Degree or Ph.D. in a relevant field.

Relevant Fields of Study:

  • Computer Science
  • Data Science
  • Statistics
  • Mathematics
  • Economics
  • Engineering

Experience Requirements

Typical Experience Range:

  • 12-15+ years of progressive experience in data-related roles, with at least 5-7 years in a leadership or management capacity overseeing data teams.

Preferred:

  • Proven track record of building and scaling a data function (people, processes, and technology) in a high-growth environment.
  • Experience transitioning a company from legacy data systems to a modern data stack.
  • Hands-on experience in a player-coach capacity, especially in earlier-stage companies.
  • Industry experience relevant to our business domain is a strong plus.
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