Chief Architect – Data Insights

5 days ago

Ellore, Andhra Pradesh, India beBeeDataEngineer Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Practice Lead – Data Practitioner

We are seeking a dynamic and technically accomplished Practice Lead – Data Engineering & Data Science to lead the development of scalable, high-performance data platforms and advanced analytics capabilities across the enterprise.

This role combines deep technical expertise in modern data engineering with a strong foundation in data science to enable data-driven innovation, operational efficiency, and AI readiness.

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:Data Platform Strategy & Innovation
  • Define and execute a modern data engineering and analytics 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 (e.g., GDPR, HIPAA) and ethical AI standards.
  • Serve as a key stakeholder in data governance councils and model risk committees.

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