Data & Analytics Engineer

Apex Service Partners•Tampa, FL

2d•Onsite

About The Position

Apex Service Partners is seeking a Data Engineer to join their growing data and analytics organization. This role will be instrumental in designing and ensuring the reliability of Apex's core data infrastructure, from data capture at the source to its transformation and utilization for downstream actions. The engineer will be responsible for integrating data from various source systems, maintaining ingestion infrastructure, designing and automating real-time processes, and evolving the data infrastructure to support AI-driven systems. Initially, the focus will be on delivering reliable pipelines, building event-based and streaming architectures, and implementing automated workflows. Over time, the role is expected to evolve into building a data platform for Agentic AI, including Ontologies, Vector Databases, and MCP Servers. The ideal candidate will be proficient in Python, event architecture, and API integration, with a forward-thinking approach to data agents and AI automation.

Requirements

Bachelor's/master’s degree in computer science, Data Engineering, Information Technology, or a related field.
5+ years of experience in data engineering, with hands-on work designing and operating event-based architectures (Azure preferred, not mandatory).
Demonstrated experience working with streaming or near-real-time data pipelines (e.g., Kafka, Event Hubs, Kinesis, Spark Streaming) is a strong plus.
Strong proficiency in Python, with a focus on building data ingestion frameworks and pipelines. Spark is a plus but not mandatory.
Proficiency in Python, including experience writing event-driven handlers or functions (e.g., via Azure Functions or AWS Lambda).
Exposure to agentic or LLM-based automation frameworks (e.g., LangChain, Semantic Kernel) is a plus, especially where the system needs to decide the appropriate action rather than follow fixed rules.
Exposure to graph databases (e.g., Neo4j, CosmosDB Gremlin API) is highly desirable but not a requirement candidate without this background will still be considered strongly.
Experience integrating and orchestrating data across multiple APIs is required.
Experience building or contributing to data agents or automated data workflows is preferred.
Experience with event/messaging infrastructure such as Kafka, Azure Event Hubs, or Azure Service Bus, including familiarity with Change Data Capture (CDC) patterns for detecting source-system changes.
Hands-on experience with a workflow orchestration tool (e.g., Azure Logic Apps, Airflow or similar tool) to encode conditional, multi-step automation logic.
Experience building automation with reliability and safety in mind, error handling, logging/observability, and rollback-safe design for actions with real operational impact.
Familiarity with the Microsoft Fabric/Azure ecosystem (OneLake, Lakehouses, Data Pipelines) is a plus.
Experience working with Marketing Tech Stack data (attribution, campaign performance data, customer journey data) is a strong plus.

Nice To Haves

Azure Event Hubs/Service Bus experience
Spark experience
Agentic or LLM-based automation frameworks (e.g., LangChain, Semantic Kernel)
Graph databases (e.g., Neo4j, CosmosDB Gremlin API)
Microsoft Fabric/Azure ecosystem (OneLake, Lakehouses, Data Pipelines)
Experience working with Marketing Tech Stack data (attribution, campaign performance data, customer journey data)

Responsibilities

Design, build, and maintain event-driven data architectures, enabling real-time and near-real-time data flows across systems (Azure Event Hubs/Service Bus experience preferred).
Develop and manage streaming data pipelines to support low-latency, near-real-time analytics and operational use cases.
Build and maintain robust data ingestion pipelines in Python, sourcing data from APIs, databases, and streaming platforms.
Integrate data between multiple APIs and internal systems, ensuring reliable, well-documented, and maintainable data flows.
Design and build event-driven process automation, systems that detect a business event (e.g., a status change, a record update, a threshold being crossed) and automatically trigger downstream actions across other systems.
Build agentic workflows that can interpret an event, decide on the appropriate action(s), and execute them via API calls with appropriate logging, error handling, and human-in-the-loop checkpoints where needed.
Where relevant, apply graph database concepts for relationship-based data modeling desirable but not required for this role.
Partner with the BI/analytics team to ensure upstream data structures support downstream semantic models and reporting needs.
Own data quality and pipeline reliability end-to-end; nothing ships until accuracy has been verified.
Contribute to the long-term data architecture roadmap as the function and business continue to scale.
Manage multiple concurrent data engineering projects without losing momentum, maintaining clear priorities and consistent follow-through.