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    Data Engineer - St Louis, United States - Envision

    Envision
    Envision St Louis, United States

    2 weeks ago

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    Description

    Data Engineer

    Onsite in St. Louis, MO, DIRECT HIRE, no C2C, must be a US Citizen.

    Needs to be a utility player in the data space. Hands on but also able to think, come up with solutions, be self-directed. They are trying to build a new data team so there isnt a lot of room for people to hide.

    Qualifications:

    • Heavy on SQL experience
    • 5-7 years of experience as a data engineer building core datasets and supporting business verticals as needed. Passionate about analytics use cases, data models, and solving complex data problems.
    • Hands-on experience shipping scalable data solutions in the Azure Cloud (e.g., AWS, GCP, Azure), across multiple data stores (e.g., Snowflake, Redshift, Hive, SQL/NoSQL, columnar storage formats), and methodologies (e.g., dimensional modeling, data marts, star/snowflake schemas).
    • SQL expertise with an understanding of aggregation functions, window functions, UDFs, self-joins, partitioning, and clustering approaches to run correct and highly-performant queries.
    • Highly comfortable with object-oriented programming paradigms (e.g., Python, Java, Scala).
    • Experience working at a fast-growing company, a construction company, or eagerness to contribute in such an environment (Preferred).
    • Hands-on experience in designing and building highly scalable and reliable data pipelines using Big Data stack (e.g., Airflow, Azure Data Pipeline, Spark, Hive, Parquet/ORC, Protobuf/Thrift, etc.) (Preferred).

    Job Description:

    • Define processes and ETL infrastructure to transform and make data readily available across the company.
    • Build core datasets to serve as unique sources of truth for various business functions including project management, estimating, procurement, finance, operations, and engineering.
    • Partner with project managers, business leaders, and internal stakeholders to understand their needs and design, build, and monitor pipelines that meet current requirements while also scaling gracefully with our growing data size.
    • Implement automated workflows to reduce manual/operational costs for stakeholders, define and uphold SLAs for timely data delivery, and move the company closer to democratizing data and a self-serve model (query exploration, dashboards, data catalog, data discovery).
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