Senior Data Engineer
First seen by Alion on Oct 26, 2026.
About the Company
USEReady helps enterprises apply AI and agentic intelligence to improve decisions, automate operations, and build smarter, more autonomous business systems. For more than a decade, we have built the foundations that make this possible by modernizing BI environments, migrating legacy platforms, improving data quality, and enabling governed, cloud-first architectures. These foundations now support the next step: AI-driven insights, automated intelligence, and agent-powered decision support that reduce complexity and accelerate outcomes. We work closely with technology leaders such as AWS, Elementum, Snowflake, Tableau, Databricks, and others to help organizations modernize analytics, strengthen governance, and deploy agentic automation with confidence. We founded in 2011 and Headquartered in New York City with 450+ experts across the United States, Canada, India, and Singapore, we serve industries including financial services, healthcare, manufacturing, government, education, and retail. Our deep expertise, player-coach delivery model, and focus on fast, measurable results make us a trusted partner for building an AI-ready enterprise.
Role Summary
USEReady is seeking a hands-on, Technical Senior Data Engineer (7–10 years experience) to design, build, and optimize scalable data pipelines and modern cloud data platforms. The ideal candidates are seasoned professionals with deep experience working across Operations, Engineering, and Finance data, bringing specialized knowledge of SCADA, PI systems, historians, telemetry, IoT, and OT/IT source system integration. This role will focus heavily on execution across Microsoft Fabric, Azure data services, Databricks, and Snowflake.
Key Responsibilities
- Operational & Industrial Data Integration: Own data acquisition, contextualization, integration, and scalability across historian, telemetry, SCADA, IoT, and future operational data platforms.
- OT/IT Source System Integration: Bridge operational technology (OT) and information technology (IT) environments, ensuring seamless integration between industrial source systems and enterprise analytical platforms.
- Cross-Functional Data Architecture: Architect and build pipelines handling complex datasets spanning Operations, Engineering, and Finance domains.
- Pipeline Development: Design and build batch and near-real-time ingestion pipelines utilizing ETL/ELT, change data capture (CDC), APIs, and event-driven integration.
- Medallion Architecture: Lead the hands-on delivery of Bronze, Silver, and Gold data layers with clear transformation boundaries, data quality controls, and lifecycle management.
- Data Access & Integration: Build and maintain enterprise data access services, APIs, and governed sharing patterns to support downstream analytics, applications, and AI workloads.
- Data Governance & Security: Embed security, data masking, encryption, role-based access controls (RBAC), lineage, and metadata tracking directly into data workflows.
- DataOps & Reliability: Maintain production data pipelines with active monitoring, alerting, observability, automated testing, data reconciliation, and incident response.
- Performance & FinOps: Optimize cloud resource consumption, query performance, and storage costs across Azure, Databricks, and Snowflake environments.
- CI/CD & Engineering Standards: Uphold engineering best practices including source control, peer reviews, infrastructure-as-code, and automated deployment pipelines.
Required Qualifications
- Experience: 7–12 years of professional software or data engineering experience with a strong focus on cloud-native data platforms.
- Domain & Functional Data: Seasoned experience working directly with Operations, Engineering, and Finance data.
- Industrial & OT Systems: Hands-on experience with SCADA, PI systems, industrial historians, telemetry, IoT, and OT/IT source system integration.
- Core Tech Stack: Hands-on proficiency with Microsoft Fabric, Azure data services, Databricks, and Snowflake.
- Engineering Tools: Advanced fluency in SQL, Python, Spark, Airflow and modern orchestration or CI/CD frameworks.
- Operational Mindset: Demonstrated experience scaling real-time/batch pipelines while maintaining strict RTO/RPO, data quality, and observability standards.