High Performance Data Architect

3 days ago

Aligarh, Uttar Pradesh, India beBeeDataStrategist Full time US$ 1,50,000 - US$ 2,50,000

Job Overview

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This position plays a pivotal role in developing and driving the adoption of scalable data platforms, advanced analytics capabilities, and AI-driven solutions across the enterprise.

The successful candidate will be responsible for leading cross-functional teams to deliver production-grade data solutions that are secure, explainable, and impactful.

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Data Platform Strategy & Innovation:

  1. Define and execute a modern data engineering strategy aligned with enterprise goals,
  2. Architect cloud-native data platforms using technologies like Spark, Kafka, Delta Lake, and Snowflake,
  3. Drive adoption of data mesh, lakehouse, and real-time streaming architectures.

Practice Building & Talent Development:

  1. Build and scale high-performing teams of data engineers, data scientists, and ML engineers,
  2. Design internal training programs and career pathways to upskill talent in data engineering, ML modeling, and analytics,
  3. Establish a 'ready-to-deploy' talent pool to support cross-functional data and AI initiatives.

Engineering & Delivery Enablement:

  1. Lead the development of reusable data pipelines, feature stores, and model training workflows,
  2. Implement CI/CD and MLOps practices for data and ML lifecycle automation,
  3. Promote modular, reusable components for data ingestion, transformation, and model deployment.

Advanced Analytics & Data Science Enablement:

  1. Enable rapid experimentation and deployment of predictive models, optimization algorithms, and statistical analyses,
  2. Foster collaboration between data scientists and engineers to operationalize models at scale,
  3. Support use cases across customer intelligence, operations, finance, and product analytics.

Governance, Quality & Responsible Data Use:

  1. Establish enterprise-wide data governance, lineage, and quality frameworks,
  2. Ensure compliance with data privacy regulations (e.g., GDPR, HIPAA) and ethical AI standards,
  3. Serve as a key stakeholder in data governance councils and model risk committees.

Requirements

  • Bachelor's or Master's degree in Computer Science, Data Engineering, Statistics, or a related field,
  • 10+ years of experience in data engineering, analytics, or data science roles,
  • Proven leadership in building and scaling data and ML engineering teams,
  • Hands-on expertise with tools like Apache Spark, Airflow, dbt, MLflow, TensorFlow/PyTorch, and cloud data platforms (AWS/GCP/Azure),
  • Strong understanding of data modeling, distributed systems, and ML lifecycle management.

Preferred Qualifications

  • Experience in building Data & AI Centers of Excellence or technical communities of practice,
  • Familiarity with GenAI, feature engineering at scale, and real-time inference systems,
  • Certifications in cloud data services and MLOps platforms,
  • Recognized thought leadership in data engineering or data science (e.g., speaking engagements, publications, OSS contributions).

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