Senior\Lead Data Engineer

Description

Schneider Electric’s strategic imperative to rapidly develop and launch a Cybersecurity Management Platform (CMP) to address the evolving needs of your OT customers. The goal is to expedite the CMP’s time-to-market with an initial Minimum Viable Product (MVP) for your internal Managed Security Services Program (MSSP) by the end of Calendar Year 2025. This will be achieved while simultaneously establishing Schneider’s internal software development capabilities to ensure the transition of the platform development. A prototype demonstration is targeted for October 2025 and final MVP release in end of December 2025.

Requirements

• Languages: Python, SQL.
• Data Processing: Spark, Flink.
• Databases: PostgreSQL, Apache Iceberg, MongoDB
• Streaming: Kafka
• Cloud Platforms: Azure
• Familiarity with cybersecurity, log/event data formats (e.g., syslog, JSON, STIX), and security telemetry is a strong plus.
• Excellent communication and stakeholder engagement skills.

Job responsibilities

As a Senior Data Engineer, you will design, build, and maintain the data pipelines and infrastructure that make data accessible, reliable, and actionable across the organization. You’ll focus on implementing robust, high-performance data solutions that enable analytics, reporting — translating architecture into production-ready systems.

1. Build and maintain pipelines: Develop and optimize ETL/ELT processes for collecting, transforming, and loading data from diverse sources into cloud data platforms.
2. Implement data architecture: Translate high-level architectural designs and business requirements into working, scalable data systems.
3. Optimize data performance: Tune queries, partitioning, indexing, and storage formats to deliver fast, cost-efficient access to data.
4. Automate data workflows: Use orchestration tools and CI/CD to automate data ingestion, transformation, and deployment processes.
5. Enable analytics and AI: Deliver clean, modeled, and validated datasets that power dashboards, reporting, and data science workloads.
6. Monitor and troubleshoot: Ensure data pipelines are observable, with strong monitoring, alerting, and logging. Handle production incidents and root cause analysis.
7. Ensure data integrity: Apply validation, deduplication, and reconciliation techniques to maintain consistent, trustworthy data.
8. Work with cross-functional teams: Collaborate with architects, analysts, DevOps, and software engineers to deliver data solutions that fit within broader systems.
9. Contribute to standards: Help define and enforce best practices in coding, testing, version control, and data documentation.
10. Mentor junior engineers: Provide guidance and technical support to develop team capabilities and code quality.