Senior Data Engineer

What we need:

Exol is seeking a Senior Data Engineer to help design, build, and evolve our cloud-native, multi-tenant data platform. As a key member of the Data Platform team, you will develop scalable data services that power analytics, operational visibility, customer-facing products, and AI-driven capabilities. Working closely with Engineering, Product, and Platform leadership, you will build reusable data pipelines, transformation frameworks, and platform services that enable multiple products and customers to securely ingest, transform, and consume data.

This role focuses on building durable platform capabilities rather than one-off reporting solutions.

What we do:

Exol is pioneering Warehousing-as-a-Service, offering outsourced warehousing operations and specializing in automated warehouse solutions. Our platform enables the effective movement of goods in cases and pallets across wholesale, retail, and general merchandising industries. Exol is an independently managed joint venture between Symbotic and SoftBank.

What you'll do:

Platform Engineering:

- Design, build, and enhance Exol's cloud-native, multi-tenant data platform.
- Develop scalable data services that support multiple customers and products while maintaining tenant isolation and platform reliability.
- Collaborate with platform architects to implement reusable engineering patterns and shared platform capabilities.

Data Engineering:

- Build and optimize batch and streaming data ingestion pipelines.
- Develop transformation models using dbt and BigQuery.




- Design and maintain scalable data models supporting operational and analytical workloads.
- Implement metadata-driven engineering practices that improve governance, lineage, discoverability, and maintainability.
- Ensure data quality through automated validation, testing, and monitoring.

Platform Automation:

- Develop Infrastructure as Code using Terraform.
- Build and maintain CI/CD pipelines using GitHub Actions or similar tooling.
- Create automated deployment, testing, and release processes for data platform components.
- Design resilient, replayable, and idempotent pipelines that support reliable production operations.
- Optimize BigQuery performance through partitioning, clustering, workload optimization, and cost management.

Operations & Reliability:

- Monitor platform health and troubleshoot production issues across data pipelines and platform services.
- Implement logging, monitoring, alerting, and observability best practices.
- Participate in root cause analysis and continuous improvement efforts.
- Collaborate with engineering teams to improve platform scalability, reliability, and operational excellence.

Collaboration:

- Partner with Product, Software Engineering, AI, and Analytics teams to deliver trusted data solutions.




- Participate in architecture discussions, design reviews, and code reviews.
- Contribute to engineering standards, technical documentation, and platform best practices.
- Mentor engineers and promote a culture of technical excellence.

What you'll need:

- Bachelor's degree in Computer Science, Engineering, Information Systems, or a related technical field.
- 5 years of professional data engineering experience.
- Experience designing and building cloud-native data solutions.
- Strong expertise with Google Cloud Platform, including BigQuery.
- Experience developing transformation frameworks using dbt.
- Strong SQL and Python programming skills.
- Experience with PostgreSQL or other relational databases.
- Experience orchestrating workflows using Apache Airflow or Cloud Composer.
- Experience implementing Infrastructure as Code using Terraform.
- Experience building CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins, or similar platforms.
- Experience developing scalable batch and streaming data pipelines.
- Strong problem-solving skills with the ability to balance scalability, reliability, and cloud cost optimization.

Preferred Qualifications:

- Experience with Pub/Sub, Datastream, Dataflow, Cloud Storage, Cloud Functions, or Cloud Run.
- Experience building multi-tenant SaaS platforms.
- Experience implementing metadata-driven governance, lineage, and data quality frameworks.
- Experience supporting AI, machine learning, or customer-facing analytics platforms.
- Experience with Kubernetes and Docker.

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