Chief Data Architecture Officer

1 day ago

Alwar, Rajasthan, India beBeeData Full time ₹ 25,00,000 - ₹ 35,00,000

Job Opportunity:

We are seeking a seasoned data architecture expert to spearhead the development of scalable, high-performance data platforms and advanced analytics capabilities across the enterprise.

You will be responsible for building a robust data practice, establishing engineering and modeling standards, and empowering cross-functional teams to deliver production-grade data solutions that are secure, explainable, and impactful.

Key Responsibilities:

  • Enterprise Data Strategy & Innovation:
    • Define and execute a modern data engineering strategy aligned with enterprise AI and digital transformation goals.
    • Architect scalable, cloud-native data platforms using technologies like Spark, Kafka, Delta Lake, and Snowflake.
    • Drive adoption of data mesh, lakehouse, and real-time streaming architectures.
  • Practice Building & Talent Development:
    • Build and scale a high-performing team of data engineers, data scientists, and ML engineers.
    • Design internal training programs and career pathways to upskill talent in data engineering, ML modeling, and analytics.
    • Establish a ready-to-deploy talent pool to support cross-functional data and AI initiatives.
  • Engineering & Delivery Enablement:
    • Lead the development of reusable data pipelines, feature stores, and model training workflows.
    • Implement CI/CD and MLOps practices for data and ML lifecycle automation.
    • Promote modular, reusable components for data ingestion, transformation, and model deployment.
  • Advanced Analytics & Data Science Enablement:
    • Enable rapid experimentation and deployment of predictive models, optimization algorithms, and statistical analyses.
    • Foster collaboration between data scientists and engineers to operationalize models at scale.
    • Support use cases across customer intelligence, operations, finance, and product analytics.
  • Governance, Quality & Responsible Data Use:
    • Establish enterprise-wide data governance, lineage, and quality frameworks.
    • Ensure compliance with data privacy regulations and ethical AI standards.
    • Serve as a key stakeholder in data governance councils and model risk committees.

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