Senior Data Engineer

3 weeks ago

Vapi, India Meril Full time

Job Summary

As a Senior Data Engineer in the AI domain, you will be responsible for designing, building, and optimizing large-scale data pipelines, ensuring high data quality, and enabling seamless integration with AI and machine learning models. You will collaborate closely with data scientists, ML engineers, and other cross-functional teams to deliver scalable data solutions that power AI innovations.

Key Responsibilities

  • Design and implement data pipelines for processing large volumes of structured and unstructured data.
  • Build, maintain, and optimize ETL processes for AI and machine learning projects.
  • Develop and maintain scalable data architectures to support real-time and batch processing of data for AI applications.
  • Ensure data integrity, consistency, and security across different data sources and storage systems.
  • Collaborate with AI/ML teams to understand data requirements and provide data engineering solutions to support model training and deployment.
  • Implement and manage data storage solutions such as data lakes, data warehouses, and NoSQL databases.
  • Perform data cleansing, transformation, and aggregation to ensure high data quality.
  • Optimize data processing workflows for performance, scalability, and cost-efficiency.
  • Monitor, troubleshoot, and resolve issues related to data pipelines and infrastructure.
  • Mentor and provide guidance to junior data engineers.

Required Skills & Qualifications

  • Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or a related field.
  • 3-5 years of experience as a Data Engineer, with a focus on building data solutions for AI/ML applications.
  • Expertise in programming languages such as Python, Java, or Scala for data processing.
  • Strong experience with big data technologies like Hadoop, Spark, Kafka, and distributed data systems.
  • Proficiency in SQL and experience with relational databases like PostgreSQL, MySQL, etc.
  • Hands-on experience with cloud platforms (AWS, Azure, GCP) and their data engineering services (e.g., Redshift, BigQuery, DataFlow, etc.).
  • Familiarity with containerization and orchestration tools like Docker, Kubernetes.
  • Experience with data modeling, data warehousing, and NoSQL databases (e.g., Cassandra, MongoDB).
  • Solid understanding of AI and machine learning data requirements and workflows.
  • Familiarity with version control systems such as Git.
  • Excellent problem-solving skills, with attention to detail and a focus on delivering high-quality solutions.
  • Strong communication and collaboration skills, with experience working in cross-functional teams.

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