Chief Cloud Data Architect

6 days ago

Bharatpur, Rajasthan, India beBeeDataEngineer Full time ₹ 1,50,00,000 - ₹ 2,00,00,000
Senior Data Engineer

We are seeking a highly skilled and experienced Senior Data Engineer to join our team. This is an exciting opportunity for a talented professional to design, develop, and optimize data ingestion, transformation, and governance frameworks, enabling real-time and batch data analytics across our data platform.

The ideal candidate will have deep knowledge of cloud-native data engineering, primarily on Microsoft Azure, and familiarity with AWS and GCP. They will be responsible for designing and implementing scalable, robust, and secure data pipelines using Azure Data Factory, Databricks, Synapse Analytics, and other technologies.

Responsibilities include developing and maintaining ETL/ELT processes for structured and unstructured data, implementing Change Data Capture (CDC), streaming pipelines, and batch ingestion, and optimizing performance and cost across data workloads and cloud environments.

Additionally, they will establish and maintain data quality frameworks, observability tools, and monitoring dashboards, define and enforce data rules, validations, anomaly detection, and reconciliation, and implement data lineage and metadata tracking using Azure Purview.

They will drive adoption of data cataloguing, profiling, and classification tools, collaborate with data stewards and compliance teams to ensure governance alignment, and implement role-based access control (RBAC), data masking, encryption, and tokenization.

Key Skills and Qualifications
  • 4+ years in data engineering with at least 2+ years on Azure cloud platform
  • Hands-on experience with Azure Data Factory, Azure Data Lake Gen2, Databricks, Synapse, Purview
  • Proficiency in SQL, PySpark, Python, and data orchestration tools
  • Strong understanding of data architecture patterns
Preferred Qualifications
  • 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

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

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