Event Data Engineering

A delivery-focused mindset, strong ownership, and the ability to balance immediate engineering priorities with long-term platform evolution.

Clear communication skills, sound analytical judgment, and confidence collaborating with business leaders, engineers, and analytics teams.

Resourcefulness, integrity, and comfort managing concurrent workstreams in a lean, fast-moving environment.

High standards for accuracy, maintainability, documentation, and operational reliability.

Key Responsibilities

Design and maintain event-driven architectures and streaming pipelines supporting real-time and near-real-time operational use cases.

Build Python ingestion frameworks integrating APIs, databases, and streaming sources; maintain documented, dependable cross-system data flows.

Implement event-triggered automation and agentic workflows with API execution, error handling, observability, rollback-safe design, and human-in-the-loop checkpoints.

Own Data Quality and pipeline reliability, verifying accuracy before deployment and partnering with analytics teams on upstream structures for semantic models.

Contribute to the architecture roadmap, including relationship-based Data Modeling and future AI-enabling infrastructure.

Required Qualifications

Bachelor’s or master’s degree in computer science, Data Engineering, Information Technology, or a related field.

Relevant data engineering experience designing and operating event-based architectures, with strong Python proficiency and API integration expertise.

Experience with messaging infrastructure such as Kafka, Azure Event Hubs, or Azure Service Bus, including Change Data Capture (CDC) patterns.

Hands-on workflow orchestration using Azure Logic Apps, Airflow, or comparable tools for conditional, multi-step automation.

Ability to build reliable automation with logging, error handling, and operational safeguards.

Preferred Qualifications

Streaming experience with Kinesis, Spark Streaming, or Spark, and event-driven handlers using Azure Functions or AWS Lambda within AWS environments.

Exposure to LLM automation through LangChain or Semantic Kernel, plus data agents and automated workflows.

Graph database exposure using Neo4j or CosmosDB Gremlin API.

Familiarity with Microsoft Fabric, OneLake, Lakehouses, Data Pipelines, and Lakehouse architecture in the Azure ecosystem.

Experience with Marketing Tech Stack data, including attribution, campaign performance, and customer journeys.