High-Performance Data Architect

2 days ago

Tiruchi, Tamil Nadu, India beBeeData Full time US$ 1,80,000 - US$ 2,10,000
Job Description">

We are seeking a highly skilled Data Engineer to join our team. The successful candidate will design, build and maintain high-performance data pipelines that process large volumes of data.

This is an excellent opportunity for a talented individual to take their skills to the next level and contribute to the growth and success of our organization.

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Required Skills and Qualifications
  • ">
  • Bachelor's degree in Computer Science, Engineering or related technical field">
  • 4+ years of experience in data engineering roles">
  • Strong Python programming skills with focus on data processing libraries">
  • Experience with big data technologies (Spark, Hadoop, etc.)">
  • Proven experience optimizing database performance (SQL or NoSQL)">
  • Knowledge of data pipeline orchestration tools (Airflow, Luigi, etc.)">
  • Understanding of performance optimization techniques and profiling tools">
  • Experience with cloud platforms (AWS, GCP, or Azure)">
  • Ability to translate business requirements into technical specifications">
  • Ability to work effectively in a collaborative team environment">

Benefits

The successful candidate will have access to a range of benefits including professional development opportunities and a competitive salary package.

Others

The ideal candidate will have a strong analytical mind and be able to communicate complex technical concepts effectively.

Key Responsibilities:
  • ">
  • Design and implement scalable batch processing systems using Python and big data technologies">
  • Optimize database performance, focusing on slow-running queries and latency improvements">
  • Use Python profilers and performance monitoring tools to identify bottlenecks">
  • Reduce P95 and P99 latency metrics across our data platform">
  • Build efficient ETL pipelines that can handle large-scale data processing">
  • Collaborate with data scientists and product teams to understand data requirements">
  • Monitor and troubleshoot data pipeline issues in production">
  • Implement data quality checks and validation mechanisms">
  • Document data architecture and engineering processes">
  • Stay current with emerging big data technologies and best practices">

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