Enterprise Analytics Solutions Architect

1 day ago

Cochin, Kerala, India beBeeData Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Job Summary

We are seeking a highly skilled Data Engineer to join our team.

The successful candidate will have a strong background in designing and developing scalable data pipelines using cloud-based technologies such as Azure and Snowflake.

The ideal candidate will possess excellent analytical and problem-solving skills, with the ability to translate business needs into potential analytics solutions. They will also be able to work effectively in an agile environment, collaborating closely with cross-functional teams including product owners, architects, and business stakeholders.

Key Responsibilities:
  • Data Pipeline Development: Design and develop robust, scalable, and high-quality data pipelines that support analytics and reporting needs.
  • Data Integration: Integrate structured and unstructured data from various sources into data lakes and warehouses.
  • ETL/ELT Pipelines: Build and maintain scalable ETL/ELT pipelines for batch and streaming data using Azure Data Factory, Databricks, Snowflake, and Azure SQL Server.
  • Collaboration: Collaborate with data scientists, analysts, and platform engineers to enable analytics and ML use cases.
Requirements:
  1. A minimum of 7 years of experience in data engineering or design experience designing, developing, and deploying scalable enterprise data analytics solutions from source system through ingestion and reporting.
  2. 5+ years of experience in ML Lifecycle using Azure Kubernetes service, Azure Container Instance service, Azure Data Factory, Azure Monitor, Azure DataBricks building datasets, ML pipelines, experiments, logging, and monitoring.
  3. 5+ years of experience in data engineering using Snowflake.
  4. Proficiency in SQL and any one other programming language (e.g., R, Python, C++, Minitab, SAS, Matlab, VBA).
  5. Knowledge of optimization engines such as CPLEX or Gurobi.
  6. Strong experience with cloud platforms (AWS, Azure, etc.) and containerization technologies (Docker, Kubernetes).
  7. Experience with CI/CD tools such as GitHub Actions, GitLab, Jenkins, or similar tools.
  8. Familiarity with security best practices in DevOps and ML Ops.
  9. Experience in developing and maintaining APIs (e.g.: REST).
  10. Agile/Scrum operating experience using Azure DevOps.

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