Highly Skilled Data Professional

2 weeks ago

Surat, Gujarat, India beBeeDataEngineer Full time ₹ 18,00,000 - ₹ 21,60,000
Senior Data Engineer Role

This position is responsible for designing and developing robust, scalable, and high-quality data pipelines that support analytics and reporting needs.

  1. Data Pipeline Design & Development:

Data Engineers are responsible for building and maintaining scalable ETL/ELT pipelines for batch and streaming data using cloud-based technologies. This includes:

  • Integration of structured and unstructured data from various sources into data warehouses.
  • Designing and implementing scalable database architectures to support business intelligence and machine learning workflows.
Cloud Platform Engineering:

The successful candidate will be responsible for operationalizing data solutions on cloud platforms, integrating services like Azure, Snowflake, and third-party technologies.

  • Managing environments, performance tuning, and configuration for cloud-native data solutions.
Data Modeling & Architecture:

Apply dimensional modeling, star schemas, and data warehousing techniques to support business intelligence and machine learning workflows.

  • Collaborating with solution architects and analysts to ensure models meet business needs.
Data Governance & Security:

Ensure data integrity, privacy, and compliance through governance practices and secure schema design.

  • Implementing data masking, access controls, and metadata management for sensitive datasets.
Agile Delivery:

Work closely with cross-functional teams to translate requirements into technical solutions.

  • Participating in Agile ceremonies, sprint planning, and DevOps practices for continuous integration and deployment.
Requirements
  • 7+ years of data engineering or design experience, designing, developing, and deploying scalable enterprise data analytics solutions from source system through ingestion and reporting.
  • 5+ years of experience in ML Lifecycle using cloud-based services, designing, developing and scaling complex data and feature pipelines feeding ML models and evaluating their performance.
  • Proficiency in SQL and any one other programming language (e.g., R, Python, C++, Minitab, SAS, Matlab).
  • Familiarity with security best practices in DevOps and ML Ops.
  • Experience with CI/CD tools such as GitHub Actions, GitLab, Jenkins.

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