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Director of Data Analytics (B2C/D2C)

Director of Data Analytics (B2C/D2C)

VAYUZ Technologies
  • Posted an hour ago
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Job Description

Role Responsibilities-

As the Director of Analytics, you will be:

  • Lead the next generation of our Data Mart: Redesign and optimize data models to support large-scale analytics and faster decision-making. Define the tools and frameworks for data creation, transformation, and consumption across the company.
  • Build unified data views: Integrate behavioral and transactional data into structured, query-optimized flat tables that support diverse analytics use cases.
  • Own analytics across mobile, web, marketing, product, and operations data streams: Ensure consistent data definitions and scalability.
  • Partner with business, product, marketing, CRM, operations, and finance teams: Ensure data is democratized and actionable. Establish data governance, quality standards, and best practices for the organization.
  • Drive adoption of self-service analytics: Empower teams with the right dashboards, reporting, and insights.
  • Introduce and scale AI/ML-driven insights:
  • Collaborate with product and engineering to enable LLM-powered use cases, such as conversational analytics and decision copilots.
  • Enable ML-driven use cases such as personalization, predictive modeling, and anomaly detection.
  • Build and mentor a cross-functional analytics team: The team will span business/operations analytics, marketing/CRM analytics, and product/finance analytics.
  • Partner with engineering leadership: Ensure scalability, reliability, and efficiency of the data infrastructure.
  • What We're Looking For-

    Experience

    • 8+ years in data analytics leadership, with proven success in building data marts and defining enterprise-wide data consumption strategies.
    • Strong track record of running analytics across mobile, web, marketing, product, and operations data ecosystems.
    • Deep technical expertise in data modeling, flat table structures, and query optimization for large-scale systems. Hands-on experience with both behavioral and transactional data, and integrating them into a single analytics layer.
    • Strong knowledge of modern data stack, including SQL, ETL/ELT, cloud data warehouses like Snowflake/BigQuery/Redshift, and BI tools like Looker/PowerBI/Tableau.

    AI Expertise

    • Experience working with or enabling AI/ML and LLM-based use cases, including building data pipelines for AI applications, supporting use cases such as process automation and conversational analytics, and partnering with engineering to productionise AI-driven solutions.

    ML Competency

    • Familiarity with machine learning workflows, including data pipelines for ML, feature engineering, and model monitoring, with the ability to guide data scientists in building predictive/ML models.
    • Proven ability to build and scale analytics teams, with experience in functions such as business/operations, marketing/CRM, and product/finance analytics.
    • Strong business acumen and ability to translate data into actionable insights for growth, retention, and efficiency.
    • Excellent stakeholder management and communication skills, comfortable influencing at the CXO level.

    KPIs / Success Metrics

    Success in this role will be measured by:

    Data Infrastructure Scale

    • Launch of next-gen data mart within defined timelines.
    • Reduction in data processing and reporting times.
    • Uptime and reliability of data pipelines and consumption layers.

    Adoption Impact

    • % of the organization actively using self-service analytics tools.
    • Increase in cross-team usage of standardized data views across product, marketing, finance, and operations.
    • Reduction in ad-hoc analytics requests due to robust self-service solutions.
    • LLM-powered use cases successfully deployed to production.

    Data Quality & Governance

    • % improvement in data accuracy and consistency across sources.
    • Establishment and adoption of data governance frameworks.

    Team Leadership

    • Successful hiring and scaling of a multi-disciplinary analytics team.
    • Retention and growth of top talent within the analytics function.

    More Info

    Job Type:
    Industry:
    Employment Type:

    Key Skills

    Flat table structures

    Looker

    Feature engineering

    Predictive ML models

    Data pipelines

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