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Data Engineer

2 months ago

Columbus, United States Ringside Talent Full time

Our Central Ohio Client is actively seeking a Data Engineer to join their team. The ideal candidate aligns with the responsibilities and qualifications outlined below.


Responsibilities:

  • Design, build, and maintain scalable and efficient data pipelines to extract, transform, and load (ETL) data from various sources into our data infrastructure.
  • Develop and optimize data warehouse and data lake architectures to ensure efficient storage, retrieval, and analysis of large datasets.
  • Collaborate with data analysts, data scientists, and other stakeholders to understand data requirements and implement data integration solutions.
  • Develop and implement data quality processes and measures to ensure the accuracy, completeness, and consistency of data within our systems.
  • Identify and resolve performance issues within the data infrastructure, optimize data processing and query performance, and implement data caching strategies where applicable.
  • Ensure data security and compliance with regulatory requirements by implementing appropriate access controls, data encryption, and data anonymization techniques.
  • Establish and enforce data governance policies, standards, and best practices to maintain data integrity, privacy, and security.
  • Investigate and resolve data-related issues, provide technical support to users, and perform root cause analysis to prevent future occurrences.

Qualifications:

  • 3+ years of experience as a Data Engineer or in a similar role, with a strong background in data management, ETL processes, and database technologies.
  • Proficiency in programming languages such as Python, Java, or Scala for data processing and manipulation.
  • Experience with data integration tools, such as Apache Kafka, Apache Nifi, or Informatica.
  • Strong understanding of data modeling, database design principles, and SQL.
  • Knowledge of data warehouse architectures (e.g., Kimball, Inmon) and experience with data warehousing technologies like Snowflake, Redshift, or BigQuery.
  • Familiarity with distributed computing frameworks (e.g., Hadoop, Spark) and cloud platforms (e.g., AWS, Azure, GCP).
  • Experience with version control systems (e.g., Git) and agile software development methodologies.
  • Solid understanding of data security and privacy principles and experience implementing data governance practices.