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Data Architect / Data Engineering Consultant

Reuben Cooley, Inc.

  • Irving, TX
  • 5 days ago

    Highlights

    The consultant will be responsible for understanding end-to-end data flows, developing and troubleshooting data pipelines, optimizing distributed processing workloads, performing SQL-based data reconciliation, and supporting production data platforms. We are looking for a hands-on Data Architect / Data Engineering Consultant with strong expertise in Python, PySpark, Spark optimization, distributed data processing, Hive/Impala, SQL, and production ETL troubleshooting .

    Numbers & Facts

    LocationIrving, TX

    Description

    We are looking for a hands-on Data Architect / Data Engineering Consultant with strong expertise in Python, PySpark, Spark optimization, distributed data processing, Hive/Impala, SQL, and production ETL troubleshooting.
    The consultant will be responsible for understanding end-to-end data flows, developing and troubleshooting data pipelines, optimizing distributed processing workloads, performing SQL-based data reconciliation, and supporting production data platforms.
    Required Qualifications
    • 12-15 years in data architecture, data engineering, or enterprise architecture.
    • 7 or more Hands-on development background.
    • Experience with Databricks and Snowflake. And/or-
    • Strong expertise in data warehousing, Lakehouse architecture, ETL/ELT, data modelling, and event-driven integration.
    • Experience in regulated financial services.
    Key Responsibilities
    • Design, develop, enhance, and troubleshoot ETL/ELT data pipelines.
    • Develop and maintain data processing solutions using Python and PySpark.
    • Work extensively with Apache Spark, including performance tuning and optimization.
    • Analyze Spark jobs to identify performance bottlenecks related to partitions, shuffles, joins, data skew, caching, serialization, and resource utilization.
    • Work with distributed data processing concepts and large-volume datasets.
    • Develop complex SQL queries for data transformation, validation, reconciliation, and troubleshooting.
    • Perform source-to-target reconciliation and investigate data discrepancies.
    • Work with Hive and Impala for querying and processing large datasets.
    • Troubleshoot production ETL failures, data quality issues, performance problems, and batch-processing failures.
    • Perform root-cause analysis and implement permanent fixes for recurring production issues.
    • Understand and troubleshoot end-to-end data flows, from source systems through ETL processing to downstream consumers.
    • Work with Linux environments, shell commands, batch processing, and job scheduling.
    • Collaborate with engineering, application, and business teams to resolve complex data issues.
    • Participate in technical design discussions and provide recommendations for scalable and maintainable data solutions.
    • Document technical designs, data flows, troubleshooting procedures, and production resolutions

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