Engineer, Data

Holley Performance-posted 3 months ago
New Braunfels, TX
1,001-5,000 employees
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This role focuses on backend development and integrations for building and maintaining enterprise data warehouses and data lakes. The ideal candidate will possess a deep understanding of data architecture, ETL pipelines, and integration technologies, ensuring seamless data flow and accessibility across the organization.

  • Design, develop, and maintain scalable backend systems to support data warehousing and data lake initiatives.
  • Build and optimize ETL/ELT processes to extract, transform, and load data from various sources into centralized data repositories.
  • Develop and implement integration solutions for seamless data exchange between systems, applications, and platforms.
  • Collaborate with data architects, analysts, and other stakeholders to define and implement data models, schemas, and storage solutions.
  • Ensure data quality, consistency, and security by implementing best practices and monitoring frameworks.
  • Monitor and troubleshoot data pipelines and systems to ensure high availability and performance.
  • Stay up-to-date with emerging technologies and trends in data engineering and integration to recommend improvements and innovations.
  • Document technical designs, processes, and standards for the team and stakeholders.
  • Bachelor’s degree in Computer Science, Engineering, or a related field; equivalent experience considered.
  • Proven experience as a Data Engineer with 5 or more years of experience; or in a similar backend development role.
  • Strong proficiency in programming languages such as Python, Java, or Scala.
  • Hands-on experience with ETL/ELT tools and frameworks (e.g., Apache Airflow, Talend, Informatica, etc.).
  • Extensive knowledge of relational and non-relational databases (e.g., SQL, NoSQL, PostgreSQL, MongoDB).
  • Expertise in building and managing enterprise data warehouses (e.g., Snowflake, Amazon Redshift, Google BigQuery) and data lakes (e.g., AWS S3, Azure Data Lake).
  • Familiarity with cloud platforms (AWS, Azure, Google Cloud) and their data services.
  • Experience with API integrations and data exchange protocols (e.g., REST, SOAP, JSON, XML).
  • Solid understanding of data governance, security, and compliance standards.
  • Strong analytical and problem-solving skills with attention to detail.
  • Excellent communication and collaboration abilities.
  • Certifications in cloud platforms (AWS Certified Data Analytics, Azure Data Engineer, etc.)
  • Experience with big data technologies (e.g., Apache Hadoop, Spark, Kafka).
  • Knowledge of data visualization tools (e.g., Tableau, Power BI) for supporting downstream analytics.
  • Familiarity with DevOps practices and tools (e.g., Docker, Kubernetes, Jenkins).
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