Senior Data Engineer
Design and build scalable data pipelines on Databricks using Spark, Delta Lake, and Lakehouse architecture to power enterprise reporting, analytics, and AI solutions.
- Compensation
- $142.5K–$160K / yr
- Location
- Remote, FL
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Data engineering
- Posted
Skills
Must have
- Bachelor's degree in Computer Science, Engineering, Information Systems, or related field.
- 7+ years of Data Engineering experience.
- 3+ years of hands-on Databricks experience.
- Strong expertise in Apache Spark (PySpark and SQL).
- Deep knowledge of Delta Lake, Unity Catalog, Workflows, and Lakehouse architecture.
- Advanced SQL development skills.
- Experience building enterprise-scale ETL/ELT pipelines.
- Experience with Azure cloud services.
- Strong Git, CI/CD, and DevOps practices.
- Experience with performance tuning and optimization.
Nice to have
- Insurance or Financial Services experience.
- Data Vault 2.0 certification or implementation experience.
- Experience with dbt, Terraform, and Azure DevOps.
- Experience building metadata-driven frameworks.
- Experience with Master Data Management (MDM).
- Experience developing enterprise semantic models.
- Familiarity with data quality frameworks and data governance.
- Experience enabling AI and analytics through trusted enterprise data products.
Full description
Our Company
Explore how you can contribute at AmeriLife.
For over 50 years, AmeriLife has been a leader in the development, marketing and distribution of annuity, life and health insurance solutions for those planning for and living in retirement.
Associates get satisfaction from knowing they provide agents, marketers and carrier partners the support needed to succeed in a rapidly evolving industry.
Job Summary
We are seeking an experienced Senior Data Engineer to design, build, and optimize enterprise data products and modern data pipelines on Databricks. This role is responsible for developing scalable, metadata-driven data solutions that power enterprise reporting, analytics, AI, operational applications, and business intelligence.
The ideal candidate has deep expertise in Databricks, Apache Spark, Delta Lake, and modern Lakehouse architecture, with strong experience designing enterprise-grade data engineering solutions.
Job DescriptionWhat You'll DoEnterprise Data Engineering
- Design and develop scalable Spark-based ETL and ELT pipelines.
- Build enterprise data products using Medallion Architecture.
- Develop Bronze, Silver, Gold, and Semantic data layers.
- Engineer reusable enterprise data assets for business consumption.
- Optimize large-scale distributed data processing workloads.
Databricks Platform Development
- Build native Databricks solutions using Spark, Delta Lake, Unity Catalog, Workflows, SQL Warehouses, and Delta Live Tables.
- Optimize cluster performance and workload efficiency.
- Implement automated deployment pipelines and CI/CD practices.
- Develop scalable Lakehouse solutions.
Data Modeling
- Design dimensional models for business consumption.
- Build Data Vault 2.0 models for enterprise integration.
- Develop semantic models and curated data products.
- Support Master Data Management initiatives.
Data Quality & Governance
- Implement enterprise data quality validations.
- Develop metadata-driven engineering solutions.
- Enable lineage, governance, and traceability.
- Ensure production-ready engineering standards.
Collaboration
- Partner with Architects, Product Owners, Business Analysts, and Data Scientists.
- Participate in architecture reviews and code reviews.
- Mentor junior engineers.
- Drive engineering best practices across the organization.
Required Qualifications
- Bachelor's degree in Computer Science, Engineering, Information Systems, or related field.