Chief Data Engineering Officer

15 hours ago

Erode, Tamil Nadu, India beBeeDataArchitect Full time ₹ 30,00,000 - ₹ 40,00,000

Job Title: Senior Data Architect

About the Role:

We are seeking a highly skilled Senior Data Architect with expertise in cloud-native data engineering and familiarity with various cloud platforms. The ideal candidate will have deep knowledge of building scalable data pipelines, implementing enterprise-grade data governance, security, and AI-powered engineering automation.

This role plays a pivotal part in designing, developing, and optimizing data ingestion, transformation, and governance frameworks, enabling real-time and batch data analytics across our platform.

Key Responsibilities:
  • Design and implement scalable, robust, and secure data pipelines using Azure Data Factory, Databricks, Synapse Analytics, and other relevant technologies.
  • Develop and maintain ETL/ELT processes for structured and unstructured data.
  • Implement Change Data Capture (CDC), streaming pipelines, and batch ingestion.
  • Work with AWS Glue, S3, Redshift, or GCP BigQuery as needed.
  • Optimize performance and cost across data workloads and cloud environments.
  • Establish and maintain data quality frameworks, observability tools, and monitoring dashboards.
  • Define and enforce data rules, validations, anomaly detection, and reconciliation.
  • Implement data lineage and metadata tracking using Azure Purview.
  • Drive adoption of data cataloguing, profiling, and classification tools.
  • Collaborate with data stewards and compliance teams to ensure governance alignment.
Qualifications & Experience:
  • 4+ years in data engineering with at least 2+ years on cloud platforms.
  • Hands-on experience with Azure Data Factory, Databricks, Synapse, and Purview.
  • Proficiency in SQL, PySpark, Python, and data orchestration tools.
  • Strong understanding of data architecture patterns.
  • Familiarity with Snowflake, BigQuery, Redshift, or AWS Glue.
  • Experience with data versioning and GitOps for data.
  • Working knowledge of data observability, lineage, cataloguing, data quality.
  • Exposure to privacy-enhancing techniques, access control, security auditing.
  • Exposure to machine learning use cases in data engineering pipelines, data quality, anomaly detection, and schema change detection.
  • Exposure to GenAI or agentic AI to automate data cataloguing and metadata enrichment.

The ideal candidate should possess strong problem-solving, communication, and stakeholder collaboration skills, ability to lead data initiatives and mentor team members, proactive in learning and adopting emerging technologies in data and AI.

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