Job Description

Kforce has a client that is seeking a Senior Data Engineer in Fort Lauderdale, FL to join a highly collaborative data and analytics team. Overview: This role is responsible for designing, building, and optimizing scalable data platforms that support enterprise reporting, analytics, and data-driven decision-making. The ideal candidate brings deep expertise in cloud-based data engineering, distributed data processing, and real-time streaming technologies while demonstrating strong leadership and mentoring capabilities. Responsibilities:

• Lead and mentor data engineers, promoting best practices in software development, architecture, and deployment
• Design, develop, and maintain enterprise data lake and data warehouse solutions
• Build scalable data pipelines that process structured and semi-structured data across diverse sources
• Develop and optimize cloud-native data solutions using AWS, Azure, or GCP services
• Architect and support high-performance distributed data processing frameworks for large-scale workloads
• Design and maintain real-time streaming solutions using technologies such as Apache Kafka and Apache Flink
• Collaborate with data architects, analysts, and business stakeholders to deliver reliable and scalable data products
• Monitor, troubleshoot, and optimize data pipelines, processing frameworks, and cloud infrastructure
• Drive continuous improvement initiatives by evaluating and implementing emerging technologies and best practices
• 7+ years of experience in Data Engineering or a related field
• Proven experience leading, mentoring, or guiding data engineering teams
• Strong experience building and maintaining enterprise data lakes and data warehouses
• Expertise designing and implementing scalable ETL/ELT and data integration solutions
• Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud Platform
• Experience with cloud storage and compute services, including solutions such as S3, Azure Blob Storage, Databricks, EC2, or Lambda
• Advanced experience with Apache Spark and distributed data processing frameworks
• Strong knowledge of real-time data processing and streaming technologies, including Kafka and Flink
• Proficiency in Python and SQL
• Experience developing automated data workflows and large-scale data transformation processes
• Strong analytical, troubleshooting, and communication skills

• Experience developing Delta Lakehouse solutions using Databricks
• Expertise optimizing Apache Spark performance through partitioning, caching, tuning, and troubleshooting
• Working knowledge of Java
• Experience supporting low-latency, high-throughput data environments
• Familiarity with modern data architecture patterns and cloud-native platforms
• Passion for emerging technologies and continuous innovation in data engineering

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