Senior Data Engineer

Tiger Analytics LLC

  • Dallas, TX
  • 2 days ago

    Highlights

    You will work closely with Agile engineering, architecture, and product teams to build reliable, scalable data solutions spanning data ingestion, transformation, orchestration, event streaming, and downstream data delivery. Tiger Analytics is looking for an experienced Data Engineer to design, build, and operate large-scale batch and real-time data pipelines that power enterprise data and analytics platforms.

    Numbers & Facts

    LocationDallas, TX

    Description

    Tiger Analytics is a fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.

    Tiger Analytics is looking for an experienced Data Engineer to design, build, and operate large-scale batch and real-time data pipelines that power enterprise data and analytics platforms.

    You will work closely with Agile engineering, architecture, and product teams to build reliable, scalable data solutions spanning data ingestion, transformation, orchestration, event streaming, and downstream data delivery. The ideal candidate is someone who enjoys solving complex data engineering problems and building highly available systems that operate at enterprise scale.

    Key Responsibilities:

    • Design and develop high-volume batch and real-time data pipelines for enterprise applications and analytics platforms.
    • Build end-to-end data solutions covering ingestion, transformation, orchestration, streaming, and downstream delivery.
    • Develop event-driven and real-time data processing solutions using technologies such as Kafka and Spark.
    • Build scalable data processing solutions using distributed computing frameworks such as Spark, EMR, Hadoop, or equivalent technologies.
    • Develop applications and data solutions using Java, Python, and SQL.
    • Implement and manage workflow orchestration and scheduling for complex data pipelines.
    • Work with cloud data warehouse and cloud-native data platforms to support enterprise-scale workloads.
    • Design solutions with a strong focus on reliability, scalability, performance, security, and low latency.
    • Implement secure approaches for secrets management, credentials, and service-to-service authentication in production environments.

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