Data Analytics Engineer: Scalable Data Pipelines

HealthTronics, Inc.; Austin, Northern (TX, KY); Hybrid; USD 110,000 - 160,000; Full time

Job summary

HealthTronics, Inc. in Austin, TX, is seeking a Data Analytics Engineer to design, build, and maintain scalable data pipelines, analytics platforms, and reporting solutions that drive data-informed decisions.

This role blends data engineering with business intelligence, transforming raw data into trusted insights. Collaborate with stakeholders, IT teams, and leadership to deliver dashboards, KPIs, data models, and governance practices that improve data quality and analytics adoption.

Qualifications

• Bachelor's degree in CS/IS/data analytics/engineering/math or related field.
• 3+ years in data engineering, analytics engineering, or BI roles.
• Strong SQL development and database management skills.
• Experience with data warehouses and cloud data platforms.
• Proficiency in Python and SQL; experience with BI tools like Sigma or Tableau.

Responsibilities

• Design, build, and maintain ETL/ELT processes for ingesting, transforming, and integrating data from multiple sources.
• Develop dashboards and reports using modern BI tools and translate business requirements into scalable solutions.
• Develop KPIs, metrics, and reporting frameworks to measure business performance.
• Collaborate with stakeholders to understand reporting needs and ensure data quality and governance.

Skills

SQL development

Data modeling

Stakeholder communication

Python

Data governance

Education

Bachelor's degree in Computer Science, Information Systems, Data Analytics, Engineering, Mathematics, or a related field

Tools

Sigma

Tableau

Azure Data Factory

Microsoft Fabric

Synapse Analytics

Databricks

Snowflake

Job description

HealthTronics, Inc. in Austin, TX, is seeking a Data Analytics Engineer to design, build, and maintain scalable data pipelines, analytics platforms, and reporting solutions that drive data-informed decisions.

This role blends data engineering with business intelligence, transforming raw data into trusted insights. Collaborate with stakeholders, IT teams, and leadership to deliver dashboards, KPIs, data models, and governance practices that improve data quality and analytics adoption.