Henderson Engineers
Data Analytics Engineer
Henderson Engineers, Kansas City, Missouri, United States, 64101
Data Analytics EngineerKansas City, 1801 Main Street, Kansas City, Missouri, United States of America * Lenexa, 8345 Lenexa Drive, Lenexa, Kansas, United States of America
Req #490
As a data analytics engineer, you will ensure that large volumes of data from various databases and systems are ingested, transformed, and made "analytics ready."
This position involves designing, implementing, and maintaining scalable data pipelines and analytics systems using modern programming tools. You will model raw data into clean, tested, and reusable datasets that adhere to best practices, enabling business stakeholders to easily access and derive insights from data stored in cloud-based data warehouses or on-premises databases.
You will work closely with data analysts, data scientists, and business teams to understand data requirements and ensure that solutions meet the organizations analytics and reporting needs.
Key Responsibilities:
Design, develop, and maintain efficient data pipelines to support extract, transform, load (ETL) processes.
Utilize SQL Server in an Azure environment.
Build data aggregation pipelines using Python, Apache Airflow, Spark, SQL, or ClickHouse.
Implement and manage analytics systems, data warehouses, or data lakes for structured and unstructured data.
Ensure data is well defined, transformed, tested and documented for analytical purposes.
Create and optimize data models and architectures to support analytics and reporting.
Identify and implement optimization to enhance query performance, reduce processing time, and increase overall productivity.
Work with data scientists, AI engineers, and business stakeholders to integrate predictive models, machine learning algorithms, and deliver comprehensive reports, dashboards, and visualizations.
Use Power BI to create and maintain dashboards, visualizations and reports.
Pursues challenging assignments and new knowledge.
Encourages experimentation when faced with new challenges.
Examines mistakes, clarifies lessons learned, and applies to future work.
Required Qualifications:
Bachelor's degree in computer science, data science, software engineering, or a related field. 4 additional years of experience may be considered in lieu of degree.
Minimum of 4 years of experience in data analytics, data engineering, or software engineering.
Expertise in data modeling, ETL development, and data analysis.
Strong ability in SQL for data extraction and manipulation, and proficiency in data warehousing concepts/tools such as Redshift and Snowflake.
Proficiency with DBT (data build tool) required with hands-on experience transforming data, managing models, and maintaining high-quality data pipelines.
Familiarity with cloud-based architecture such as Azure, AWS or (GCP) Google Cloud Platform for data storage and processing is strongly preferred.
Preferred Qualifications:
Data science or data analytics experience highly preferred.
Experience with data vault 2.0 methodology and experience implementing data vault 2.0 for scalable and adaptable data warehousing solutions is strongly preferred.
Substantial programming ability using languages/tools such as R, Python, Java Script, Scala and C++ for data manipulation and scripting.
Previous experience in the AEC industry.
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Req #490
As a data analytics engineer, you will ensure that large volumes of data from various databases and systems are ingested, transformed, and made "analytics ready."
This position involves designing, implementing, and maintaining scalable data pipelines and analytics systems using modern programming tools. You will model raw data into clean, tested, and reusable datasets that adhere to best practices, enabling business stakeholders to easily access and derive insights from data stored in cloud-based data warehouses or on-premises databases.
You will work closely with data analysts, data scientists, and business teams to understand data requirements and ensure that solutions meet the organizations analytics and reporting needs.
Key Responsibilities:
Design, develop, and maintain efficient data pipelines to support extract, transform, load (ETL) processes.
Utilize SQL Server in an Azure environment.
Build data aggregation pipelines using Python, Apache Airflow, Spark, SQL, or ClickHouse.
Implement and manage analytics systems, data warehouses, or data lakes for structured and unstructured data.
Ensure data is well defined, transformed, tested and documented for analytical purposes.
Create and optimize data models and architectures to support analytics and reporting.
Identify and implement optimization to enhance query performance, reduce processing time, and increase overall productivity.
Work with data scientists, AI engineers, and business stakeholders to integrate predictive models, machine learning algorithms, and deliver comprehensive reports, dashboards, and visualizations.
Use Power BI to create and maintain dashboards, visualizations and reports.
Pursues challenging assignments and new knowledge.
Encourages experimentation when faced with new challenges.
Examines mistakes, clarifies lessons learned, and applies to future work.
Required Qualifications:
Bachelor's degree in computer science, data science, software engineering, or a related field. 4 additional years of experience may be considered in lieu of degree.
Minimum of 4 years of experience in data analytics, data engineering, or software engineering.
Expertise in data modeling, ETL development, and data analysis.
Strong ability in SQL for data extraction and manipulation, and proficiency in data warehousing concepts/tools such as Redshift and Snowflake.
Proficiency with DBT (data build tool) required with hands-on experience transforming data, managing models, and maintaining high-quality data pipelines.
Familiarity with cloud-based architecture such as Azure, AWS or (GCP) Google Cloud Platform for data storage and processing is strongly preferred.
Preferred Qualifications:
Data science or data analytics experience highly preferred.
Experience with data vault 2.0 methodology and experience implementing data vault 2.0 for scalable and adaptable data warehousing solutions is strongly preferred.
Substantial programming ability using languages/tools such as R, Python, Java Script, Scala and C++ for data manipulation and scripting.
Previous experience in the AEC industry.
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