Data Engineer - Databricks

Fintech•Tampa, FL

8h•Hybrid

About The Position

Fintech is seeking a Data Engineer with strong Databricks expertise to modernize and scale its Business Intelligence capabilities. The role designs and builds data pipelines, deploys machine-learning solutions, and operationalizes intelligent analytics to support organizational decision-making. It combines data-engineering best practices with applied machine learning, MLOps, and AI. The position is hybrid in Tampa, Florida. The role includes end-to-end data pipeline and integration ownership; advanced SQL, Kafka streaming, Databricks workflow development using medallion architecture, governance, compliance, performance tuning, and data security; Apache NiFi development for invoice and PO processing; and integration with Druid, MongoDB, OpenSearch, and Postgres. It also builds and maintains machine-learning pipelines for model training, deployment, and monitoring; optimizes architectures for large-scale ML workloads; and explores LLM-based solutions, RAG architectures, and generative AI for business use cases. The Data Engineer collaborates across product and engineering, mentors junior engineers, communicates with stakeholders about data requirements, and works directly with data-platform vendors to evaluate tools, provide technical product feedback, and coordinate integrations. Fintech provides accounts-payable automation solutions. Its PaymentSource platform supports invoice payment and collection for businesses nationwide. The company is a drug-free and equal opportunity workplace and participates in E-Verify.

Requirements

Bachelor’s or master’s degree in Computer Science, Data Engineering, Statistics, or a related field.
At least 5 years of experience in data engineering.
At least 2 years applying machine learning in production.
Strong Databricks expertise.
Advanced SQL optimization and complex query design skills.
Experience with Kafka streaming applications and connector development.
Experience developing Databricks workflows with medallion architecture.
Knowledge of data governance, compliance, performance tuning, data security, and privacy best practices.
Experience with Apache NiFi pipeline development and purpose-built data stores including Druid, MongoDB, OpenSearch, and Postgres.
Ability to build and maintain machine-learning pipelines and design architectures for large-scale ML workloads.
Cross-functional collaboration, technical mentoring, analytical thinking, stakeholder communication, and a focus on process improvement and data quality.

Responsibilities

Design and build end-to-end data pipelines and integrations.
Optimize SQL and design complex queries.
Develop Kafka streaming applications and connectors.
Develop Databricks workflows using medallion architecture.
Implement data governance and compliance practices.
Tune performance for large-scale data processing.
Apply data security and privacy best practices.
Develop Apache NiFi pipelines for invoice and purchase-order processing.
Integrate with Druid, MongoDB, OpenSearch, and Postgres.
Build and maintain machine-learning pipelines for model training, deployment, and monitoring.
Design and optimize data architectures for large-scale machine-learning workloads.
Explore and implement LLM-based solutions, RAG architectures, and generative AI for business use cases.
Collaborate with product and engineering teams.
Mentor junior data engineers.
Communicate with stakeholders to understand data requirements.
Improve processes and efficiency while maintaining data accuracy and reliability.
Communicate directly with data-platform vendors, evaluate vendor tools for use cases, provide technical feedback on product roadmaps, and coordinate data integration projects.