Data Engineer – Databricks & Lakehouse (Power BI Environment)
WFA Digital Insight
The demand for skilled data engineers with expertise in Databricks and Power BI has grown significantly, with a 25% increase in job postings over the past year. As companies like Hillenbrand continue to invest in data-driven decision making, the need for professionals who can design and maintain scalable data pipelines has become crucial. With the rise of remote work, data engineers can now work with global companies from anywhere, making this role an attractive opportunity for those looking to leverage their skills in a remote setup. Candidates should be prepared to highlight their experience with data modeling, pipeline development, and data quality assurance, as well as their ability to work collaboratively with cross-functional teams.
Job Description
About the Role
The Data Engineer role at Hillenbrand is a critical position that involves designing, building, and optimizing scalable data pipelines and curated data products within a modern Lakehouse architecture using Databricks. The ideal candidate will have deep experience in Databricks, strong data modeling skills, and a focus on building reliable, testable, and governed data solutions. As a key member of the data engineering team, you will work closely with stakeholders to deliver high-quality, business-ready datasets that power enterprise analytics and Power BI reporting.The role entails working with large datasets, developing data models, and ensuring data quality and integrity. You will be responsible for collaborating with cross-functional teams, including business intelligence, data science, and product teams, to identify and prioritize data engineering projects. The team context is dynamic, with a focus on innovation, experimentation, and continuous learning.
What You Will Do
- Design, develop, and maintain scalable data pipelines using Databricks and Delta Lake
- Build and manage transformations across bronze, silver, and gold layers
- Optimize processing for performance, reliability, and cost
- Integrate data from ERP, CRM, APIs, and other enterprise systems
- Develop gold-layer datasets aligned to standardized business definitions
- Translate business requirements into reusable data models
- Ensure consistency of core metrics across reporting
- Align Databricks outputs with Power BI semantic models
- Implement automated data quality checks and validation rules
- Build testable, production-ready pipelines
- Support impact analysis using lineage tools
What We Are Looking For
- Bachelor's degree in a relevant field
- 8+ years of data engineering experience
- Strong Databricks, Spark, Delta Lake experience
- SQL and Python proficiency
- Experience with Power BI or similar tools
- Excellent data modeling and data warehousing skills
- Ability to work collaboratively with cross-functional teams
- Strong problem-solving skills and attention to detail
- Experience with data quality assurance and data governance
Nice to Have
- Experience with Azure Data Factory, Synapse, or Data Lake
- DevOps and CI/CD experience
- AI agents or AI-assisted development exposure
- Experience in large, complex enterprise environments
Benefits and Perks
- Opportunity to work with a leading global supplier of hot runners and plastic processing equipment
- Collaborative and dynamic work environment
- Professional development opportunities
- Competitive compensation package
- Flexible remote work arrangements
- Access to cutting-edge technologies and tools
- Recognition and rewards for outstanding performance
How to Stand Out
- Make sure to highlight your experience with Databricks, Power BI, and data modeling in your resume and cover letter.
- Be prepared to provide examples of your data engineering projects and how you overcame challenges.
- Showcasing your ability to work collaboratively with cross-functional teams and stakeholders is crucial.
- Familiarize yourself with Hillenbrand's products and services to understand the company's needs and goals.
- Prepare to discuss your experience with data quality assurance, data governance, and data security.
- Be ready to explain your approach to optimizing data pipelines and improving performance.
- Highlight any experience you have with AI-assisted development tools or AI agents.
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