Cubic Transportation Systems - Principal Data Engineer - ETL

We are seeking a Principal Data Engineer to lead the design, architecture, and evolution of large-scale, enterprise data platforms. This role requires deep technical expertise in data engineering, distributed systems, and cloud-native architectures, along with the ability to influence technical direction across teams.

As a Principal Data Engineer, you will be responsible for defining data architecture standards, driving platform scalability and reliability, and mentoring senior and mid-level data engineers while remaining hands-on with critical system components.

Key Responsibilities :

- Architect and evolve highly scalable, reliable, and secure data platforms supporting analytics, reporting, and downstream systems.

- Define and enforce data engineering best practices, standards, and reference architectures.

- Design end-to-end data pipelines (batch and streaming) across data lakes, warehouses, and real-time systems.

- Own architectural decisions around data modelling, partitioning, schema evolution, and performance optimization.

- Act as a technical authority for data engineering across multiple teams and projects.

- Review and guide complex designs, ensuring alignment with scalability, reliability, and cost-efficiency goals.

- Mentor Senior and mid-level Data Engineers through design reviews, code reviews, and technical coaching.

- Drive adoption of modern data engineering practices, tools, and frameworks.

- Lead the development of production-grade ETL/ELT pipelines using Python, SQL, and distributed processing frameworks.

- Work extensively with Spark, Databricks, or equivalent big data platforms.

- Design and optimize data lake and data warehouse architectures.

- Ensure strong data quality, lineage, observability, and monitoring across pipelines.

- Design and operate cloud-native data platforms on AWS, Azure, or GCP.

- Optimize platform performance, scalability, and cost through efficient cloud resource usage.

- Collaborate with DevOps and Platform teams to improve CI/CD, infrastructure automation, and reliability.

- Partner with product, analytics, and engineering teams to translate business requirements into robust data solutions.

- Influence roadmap decisions through technical insights and trade-off analysis.

- Ensure data platforms meet enterprise security, governance, and compliance standards.

Required Skills & Experience :

- 9-12 years of hands-on experience in data engineering or data platform roles.

- Proven experience designing and owning large-scale data platforms in production.

- Expert-level proficiency in SQL and Python (or Scala).

- Strong experience with distributed data processing frameworks (Apache Spark, Databricks, etc.).

- Deep understanding of data modelling concepts (dimensional, normalized, denormalized).

- Experience with data lakes, data warehouses, and modern lakehouse architectures.

- Strong knowledge of data quality frameworks, metadata management, and lineage.

- Extensive experience with at least one major cloud platform: AWS, Azure, or GCP.

- Hands-on experience with orchestration tools such as Airflow, dbt, or equivalent.

- Strong understanding of CI/CD, Git-based workflows, and infrastructure automation.

- Experience building fault-tolerant, observable, and maintainable data systems.

- Strong troubleshooting, performance tuning, and optimization skills.

- Ability to evaluate and introduce new technologies responsibly.

Good to Have :

- Experience with real-time / streaming data systems (Kafka, Kinesis, Event Hubs).

- Exposure to data governance, security, and regulatory compliance frameworks.

- Experience working in high-scale, data-driven product environments.

- Prior experience influencing organization-wide data strategy.