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Data Eng & BI Analyst

Job in Virginia Beach, Virginia, 23450, USA
Listing for: STIHL
Full Time position
Listed on 2026-07-20
Job specializations:
  • IT/Tech
    Data Engineering
Salary/Wage Range or Industry Benchmark: 85000 - 115000 USD Yearly USD 85000.00 115000.00 YEAR
Job Description & How to Apply Below

About Us

As a family‑owned company, it’s the people who continue to make STIHL a successful, world‑class brand. Since revolutionizing the forestry industry in Germany with the first electric chainsaw in 1926, the STIHL name has remained synonymous with outstanding innovations, high‑quality products and comprehensive service.

We are seeking a Data Engineer & BI Analyst to join the team. With competitive compensation, excellent benefits and exciting growth potential, it is a great time to join the STIHL team!

Why You’ll Love Working at STIHL
  • Competitive pay with multiple bonus opportunities and potential for annual merit increases
  • Excellent health benefits including Medical, Dental & Vision Insurance
  • Onsite Health & Wellness Center for employees and eligible family members
  • Company‑paid Life Insurance and Short & Long‑Term Disability
  • Robust retirement offerings including:
  • A fully vested Pension Plan after 5 years
  • A 401(k) with generous employer match
  • Paid time off including 11 Paid Holidays
  • A strong culture of stability, community, and innovation
About You

You enjoy helping build and maintain the infrastructure that powers critical business reporting, advanced analytics, and data‑driven decision‑making across the company. You thrive in complex data environments and bring clarity to ambiguous problems. You communicate effectively with both technical and non‑technical stakeholders and are naturally curious, resourceful, and eager to learn new tools and systems.

Job Duties & Responsibilities
  • Design, build, and maintain reliable data pipelines using Databricks, Delta Live Tables, and Azure Data Factory.
  • Structure and manage Power BI semantic models using Tabular Editor and deployment pipelines.
  • Diagnose and resolve data issues spanning multiple systems, including ADF, Databricks, and Power BI.
  • Apply strong debugging skills to identify root causes of data mismatches, pipeline failures, and performance issues.
  • Collaborate with stakeholders across business functions to clarify data needs and communicate technical concepts.
  • Contribute to continuous improvement of our data infrastructure, identifying and implementing scalable solutions.
Specifications
  • Bachelors degree in Computer Science, Information Systems, Statistics, Engineering or related field preferred.
  • 5+ years of experience in data engineering, BI engineering, or similar technical role.
  • Hands‑on experience building pipelines with Databricks and/or Azure Data Factory
  • Strong proficiency with Python for data manipulation and transformation
  • Working knowledge of Power BI, including dataset design, relationships, and refresh behavior.
  • Experience working in Git‑based workflows.
  • Demonstrated skill in debugging issues spanning ingestion, transformation, and reporting layers.
  • Clear written and verbal communication skills, especially when explaining technical topics to non‑technical stakeholders.
  • Strong collaboration skills - shares knowledge, coordinates effectively, and communicates blockers early.
  • Proven ability to prioritize and problem‑solve in ambiguous or high‑pressure situations.
  • Proactive approach to improving reliability, performance, or maintainability of existing systems.
  • Experience in AI, machine learning, or emerging data technologies - especially where they intersect with analytics and reporting
  • Designing and developing AI solutions using common industry cloud platforms such as Microsoft Azure, Databricks, AWS, or Fabric
  • Knowledge of Power BI Premium capacity and refresh management.
  • Exposure to metadata‑driven frameworks and config‑based pipeline logic
  • Familiarity with Unity Catalog, Terraform, or other emerging Databricks tools
  • Experience in automation, optimization, and reducing manual overhead in analytics systems.
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