Key skills
Azure Data StackData EngineeringPythonApache SparkCI/CD PipelinesData GovernanceData IngestionDevOpsLLM ApplicationBachelor’s DegreeAIETLELTData LakeAnalyticsDatabricksSparkAzureGitHub ActionsAzure DevOpsGitHubVersion ControlServiceNowSaaSSalesforceCI/CD
About this role
Intrado is dedicated to saving lives and protecting communities, helping them prepare for, respond to, and recover from critical events. In this pivotal role, the Staff Data Engineer will build the high-performance foundation for the company’s internal business analytics, ensuring timely and actionable insights for decision-making.
Responsibilities:
- Infrastructure Architecture: Design and implement the core architecture for the company’s data ecosystem that will be used for business analytics. This includes the end-to-end architecture from the data in source systems to delivery in visualizations
- High-Scale Ingestion: Build robust Azure Data Factory pipelines to pull data from disparate "Source A" systems (Salesforce, ServiceNow, D365) to "Sink B" (Azure data lake)
- Standards & Governance: Set the technical standards for the Business Operations engineering team. You will define how the team handles CI/CD, version control, and data quality testing at the ingestion level
- System Reliability: Ensure the raw and bronze data layers are available and up to date, minimizing downtime
Requirements:
- 10+ years of progressive experience in Data Engineering, with a specific focus on designing and building cloud infrastructure and high-volume data movement
- Deep expertise in architecting the Azure Data Stack (Azure Data Factory, Azure Data Lake Storage, Databricks) or comparable modern cloud data platforms and tooling
- Proven ability to build robust, scalable ELT/ETL pipelines using Azure Data Factory and Databricks or equivalent orchestration and distributed processing frameworks
- Expert-level proficiency in Python and Apache Spark for distributed data processing
- Experience implementing enterprise-grade data governance, and data lineage
- Strong experience implementing CI/CD pipelines (Azure DevOps or GitHub Actions) for data infrastructure
- Experience leveraging LLMs and AI-assisted development tools to accelerate data engineering workflows, improve code quality, and automate repetitive technical tasks
- Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or a closely related technical discipline
- Master's or equivalent in Computer Science, Engineering, or Cloud/Data Systems
- Prior experience working in a technology company or SaaS environment