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Mid-Level Data Engineer

Henderson Engineers

Providing innovative engineering solutions for buildings, infrastructure, and energy projects.

Designing and implementing scalable data pipelines and analytics systems.

17d ago

Expert & Leadership (13+ years)

Full Time

Lenexa, KS

Office Full-Time

Company Size

500 Employees

Service Specialisms

Mechanical engineering

Electrical engineering

Plumbing engineering

Fire and life safety

Refrigeration

Telecom

Security

Acoustics

Sector Specialisms

Business

Community

Health

Retail

Venue

Grocery & Distribution

Role

What you would be doing

dashboard creation

data modeling

query optimization

etl development

pipeline development

data warehousing

Use tools like Tableau, Power BI, or Looker to create and maintain dashboards, visualizations and reports.
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.
Design, develop, and maintain efficient data pipelines to support extract, transform, load (ETL) processes.
Ensure data quality and accuracy through validation, monitoring, and error-handling processes.
Implement and manage analytics systems, data warehouses, or data lakes for structured and unstructured data.
Build data aggregation pipelines using Python, Apache Airflow, Spark, SQL, or ClickHouse.
Ensure data is well defined, transformed, tested and documented for analytical purposes.
Work with data scientists, AI engineers, and business stakeholders to integrate predictive models, machine learning algorithms, and deliver comprehensive reports, dashboards, and visualizations.

What you bring

etl

sql

data vault

dbt

degree

cloud

Knowledge of ETL processes, data integration, and data warehousing concepts.
In-depth knowledge of relational databases required.
Strong critical thinking skills, and the ability to think analytically.
Bachelor’s degree in computer science, data science, software engineering, or a related field required. 4 years of related experience may be considered in lieu of degree.
Experience with data vault 2.0 methodology and experience implementing data vault 2.0 for scalable and adaptable data warehousing solutions is strongly preferred.
Strong ability in SQL for data extraction and manipulation, and proficiency in data warehousing concepts/tools such as Redshift and Snowflake.
Familiarity with data visualization tools such as Tableau, Power BI, and Looker.
Knowledge of big data technologies and distributed computing frameworks such as Hadoop and Spark.
Substantial programming ability using languages/tools such as R, Python, Java Script, Scala and C++ for data manipulation and scripting.
Proficiency with DBT (data build tool) required with hands-on experience transforming data, managing models, and maintaining high-quality data pipelines.
Minimum 4 years of experience in data analytics, data engineering, or software engineering required.
Excellent communication skills to effectively collaborate with cross-functional teams and present insights to business stakeholders.
Expertise in data modeling, ETL development, and data analysis.
Familiarity with cloud-based architecture such as Azure, AWS or (GCP) Google Cloud Platform for data storage and processing is strongly preferred.
Solid understanding of relevant data governance, data quality, and data security best practices.
Additional experience in data science or data analytics is highly preferred.

Benefits

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Training + Development

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Interview process

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