Senior Data Engineer

Posted 25ds ago

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Job Description

Senior Data Engineer designing and implementing scalable data systems for corporate technology initiatives. Collaborating cross-functionally to ensure high-quality data solutions that meet business needs.

Responsibilities:

  • Streamlining Data Integration You’ll design and automate scalable systems to ingest and transform data from diverse sources, ensuring seamless and efficient data flow across the organization.
  • Safeguarding Data Quality By implementing robust monitoring frameworks, you’ll proactively detect and resolve data integrity issues, maintaining trust in analytics and reporting.
  • Establishing Data Governance You’ll lead the development of governance processes to manage metadata, access, and retention, ensuring compliance and secure data usage for internal and external stakeholders.
  • Building Scalable Data Pipelines You’ll architect reliable and high-performance ETL/ELT pipelines with built-in monitoring and alerts, enabling timely and accurate data delivery for business needs.
  • Optimizing Database Design and Performance Through thoughtful physical data modeling and indexing strategies, you’ll enhance database efficiency and scalability for large-scale operations.
  • Modernizing Data Infrastructure You’ll develop and operate advanced storage and processing solutions using distributed and cloud platforms, supporting big data initiatives and analytics.
  • Automating Data Workflows By leveraging modern tools and techniques, you’ll reduce manual data preparation tasks, boosting productivity and minimizing errors.
  • Mentoring and Agile Collaboration You’ll coach junior team members and contribute to agile practices like DevOps and Scrum, accelerating delivery of critical analytics projects and fostering team growth.

Requirements:

  • Minimum of 5 years of hands-on experience in data engineering with expertise in Azure Databricks and programming in Scala or Python.
  • Proven experience in building and maintaining structured streaming pipelines using Spark.
  • Strong knowledge of big data technologies, including Delta Lake, Apache Spark, Structured Streaming, and SQL.
  • Experience with Git for version control and CI/CD pipeline management.
  • Nice to Have (Preferences): Data Engineering Certification (e.g., Databricks Certified Data Engineer, Apache Spark Professional Data Engineer, or equivalent).
  • Exposure to real-time data ingestion frameworks and cloud-native data services (e.g., Azure Event Hub, Azure Data Lake, AWS SQS, etc).
  • Familiarity with data governance, access control (e.g., Unity Catalog or Immuta), and performance monitoring tools in cloud environments.