High-Performance Data Systems Architect

7 days ago

davangere, India beBeeData Full time

Job Title: Senior Data EngineerThe company is looking for a highly skilled Senior Data Engineer to lead the design, build and optimization of high-performance data pipelines and systems.Key Responsibilities:Design and develop scalable and reliable ETL/ELT pipelines that support business requirements.Build and maintain data models, data marts, and data warehouses to meet analytics and reporting needs.Work with large-scale structured and unstructured datasets across multiple sources.Implement data quality, validation, and monitoring frameworks to ensure data accuracy and integrity.Optimize existing pipelines for better performance, cost efficiency, and maintainability.Collaborate with cross-functional teams to gather and implement data requirements.Write optimized SQL queries and well-structured, maintainable Python code.Support production pipelines by troubleshooting issues and performing root cause analysis.Use version control, CI/CD, and automated deployment tools for data engineering tasks.Required Skills & Qualifications:5+ years of hands-on experience in Data Engineering.Strong programming skills in Python and SQL.Experience with ETL frameworks (Airflow, dbt, Spark, Glue, Dataflow, etc.).Expertise in cloud data platforms (AWS, Azure, or GCP).Strong understanding of data modelling and data warehouse/lakehouse architectures.Experience working with big data tools (Spark, Kafka, Hadoop) is a plus.Experience with source control (Git) and CI/CD pipelines.Benefits:Opportunity to work on cutting-edge technology projects.Collaborative and dynamic work environment.Professional development and growth opportunities.Others:Must have a strong passion for data engineering and problem-solving.Must be able to work effectively in a fast-paced, collaborative environment.Preferred / Nice-to-Have Skills:Experience with Snowflake, Redshift, BigQuery, Databricks, or Synapse.Exposure to event-driven or real-time data streaming architectures.Experience integrating with ML pipelines or supporting Data Science workloads.Familiarity with containerization (Docker, Kubernetes).

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