Senior Data Engineer

Fortegra•Jacksonville, FL

49d•Onsite

About The Position

Fortegra is seeking a Senior Data Engineer to join its technology organization. The role leads modernization and remediation of legacy data platforms using existing tools and new lakehouse and cloud data platform capabilities. The engineer architects and builds scalable data pipelines on Azure, designs dimensional data warehouse models, promotes engineering best practices, and adopts AI-assisted engineering using platforms such as OpenAI and Anthropic. The position supports mission-critical, highly visible projects across the organization, with responsibility for data quality and treating data as a product. The Senior Data Engineer owns robust batch and streaming pipelines and RESTful APIs for analytics and operational workloads; develops Databricks lakehouse solutions and Azure-based ingestion and transformation workflows; and maintains enterprise data warehouse models. The role also integrates internal and third-party systems, applies AI to pipeline development and data validation, implements data quality and observability frameworks, documents architectures, establishes CI/CD and infrastructure-as-code practices, and mentors other engineers. The position requires close attention to business needs, security, reliability, and continuous improvement.

Requirements

Bachelor of Science in Computer Science, Engineering, or equivalent required.
At least 8 years of hands-on data engineering experience building and operating production data platforms.
Expert-level Databricks experience, including Apache Spark/PySpark, Delta Lake, medallion architecture, Unity Catalog, Delta Live Tables, and cluster and cost optimization.
Proficiency with the Azure data stack, particularly Azure Data Factory, and services such as ADLS Gen2, Azure Synapse, Event Hubs, Key Vault, and Azure Functions.
Strong command of data warehousing and dimensional modeling, including star schemas, conformed dimensions, slowly changing dimensions, and ETL/ELT practices.
Demonstrated experience building, consuming, and integrating RESTful APIs for data ingestion and delivery.
Practical experience using OpenAI and Anthropic platforms to improve engineering efficiency, including prompt engineering, API integration, LLM-assisted data checks, and responsible AI practices.
Deep knowledge of Python and strong SQL experience, including query tuning and performance optimization.
Experience with dbt for modular, tested, and documented transformations.
Experience deploying Azure Infrastructure as Code using Terraform or Bicep and building CI/CD pipelines using GitHub and/or Azure DevOps and other source-control environments.
Strong analytical skills working with structured, semi-structured, and unstructured datasets.
Excellent verbal, written, and interpersonal communication skills, including the ability to explain technical solutions to technical and business audiences.
Ability to influence and build relationships with engineering and data science teams, technology leadership, external service providers, infrastructure, and enterprise architecture teams.
Applicants must be authorized to work for any employer in the United States; employment visa sponsorship is unavailable now or in the future.

Nice To Haves

An advanced degree.
Experience with streaming and near-real-time data processing, such as Spark Structured Streaming, Event Hubs, or Kafka.
Familiarity with data governance, security, lineage, and observability tools such as Unity Catalog, Microsoft Purview, or Great Expectations.
Azure and/or Databricks certifications, such as Azure Data Engineer Associate or Databricks Certified Data Engineer Professional.

Responsibilities

Architect, design, develop, and own high-performance batch and streaming data pipelines and RESTful APIs for analytics and operational workloads.
Lead Databricks lakehouse design and implementation, including Delta Lake, medallion architecture, Delta Live Tables, Unity Catalog governance, and Spark performance optimization.
Build and orchestrate ingestion and transformation workflows using Azure Data Factory, Databricks Workflows, and dbt across structured, semi-structured, and unstructured sources.
Design and maintain enterprise data warehouse models using dimensional modeling practices, including star schemas, conformed dimensions, slowly changing dimensions, and fact table design.
Integrate internal and third-party systems through RESTful APIs, handling authentication, pagination, rate limiting, and schema evolution.
Use AI platforms such as OpenAI and Anthropic to accelerate pipeline development, generate and refactor transformation code, automate data-quality validation and anomaly detection, enrich and classify data, and scale deployment through AI-assisted CI/CD.
Implement automated data-quality frameworks, testing, and data observability for freshness, volume, schema drift, and lineage monitoring.
Define and document cloud solution architectures, technical designs, and diagrams; contribute to architectural decisions and evaluate system implementations.
Establish CI/CD pipelines and Infrastructure as Code for data workloads using GitHub Actions and/or Azure DevOps, including automated unit and integration testing.
Develop a thorough understanding of the business and its data strategy and assemble complex data sets that meet functional and non-functional requirements.
Mentor junior engineers, conduct code reviews, and set standards for engineering excellence, documentation, and security.
Troubleshoot data-management issues and identify opportunities for continuous improvement with customer focus, ownership, urgency, and drive.