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Staff Data Engineer
Staff Data EngineerElastic • United States, United States, United States
Staff Data Engineer

Staff Data Engineer

Elastic • United States, United States, United States
[job_card.variable_days_ago]
[job_preview.job_type]
  • [job_card.full_time]
[job_card.job_description]

What is The Role

We are seeking a Staff Data Engineer to join our Data Engineering & Architecture team. In this role, you will contribute to our mission of building a world-class Data Platform. Your work will directly impact our strategy by making it easier for internal customers to leverage data as a strategic asset. This position offers an excellent opportunity to help craft critical initiatives that support real business outcomes.

What You Will Be Doing

  • Assist in developing, implementing, and optimizing data pipelines using Apache Airflow for workflow orchestration.
  • Develop and maintain ETL / ELT processes using Apache Spark, Apache Iceberg, and dbt.
  • Implement real-time data streaming solutions using Apache Kafka and Apache Flink.
  • Use Terraform for infrastructure-as-code to deploy resources on Google Cloud Platform (GCP).
  • Document & Guide Users on Best Practices for using our platform.
  • Monitor and Audit the Data Platform to ensure policies and procedures are maintained.
  • Participate in code reviews to improve code quality and validate standard processes are met.
  • Process Access Requests ensuring all approval processes are followed.

What You Bring

  • Demonstrated interest in data engineering through personal projects, coursework, or contributions to open-source projects.
  • Strong Analytical and Problem Solving Skills.
  • Excellent Verbal and Written Communication Skills.
  • Eagerness to learn and stay updated with the latest data engineering trends.
  • Proficiency in programming languages, particularly Bash, Python, and SQL.
  • Knowledge of database systems, including OLAP, OLTP, Document, and Vector databases.
  • General understanding of data pipelines, ETL / ELT processes, data modeling, and data warehousing concepts.
  • Basic Knowledge of Git and DevOps practices.
  • Proficiency with data processing frameworks such as Kafka, Flink, and Spark.
  • Experience with Apache Iceberg and Data Lake houses
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