Remote job
Data Engineer
Job details
About this role
Role overview
Join the data engineering team powering analytics, business intelligence, operational reporting, and data-driven products across a global AI cloud platform. This senior role owns significant portions of the data platform end-to-end, turning ambiguous business needs into pragmatic technical solutions. It's a hands-on engineering position with real influence on architecture, reliability, and the way data flows through a fast-scaling AI infrastructure company.
Responsibilities
- Design, deliver, and operate complex data pipelines, datasets, and platform components that serve many internal teams. - Translate business and analytical requirements into scalable data models, reliable data products, and clear technical plans. - Evolve data architecture, storage, processing, and orchestration patterns to handle large-scale workloads. - Improve data quality, observability, lineage, and incident response for critical datasets and pipelines. - Investigate and resolve production performance, reliability, and data-correctness issues. - Build reusable tools, conventions, and automation that raise engineering productivity and reduce operational risk. - Partner with product and business stakeholders to define data contracts, priorities, and success criteria. - Contribute to technical direction through design reviews, trade-off discussions, and documentation. - Mentor other engineers through code reviews, pairing, and knowledge sharing. - Participate in on-call rotation and take ownership of the operational health of supported systems.
Requirements
- 5+ years of experience in data engineering, backend engineering, or a closely related role delivering production data systems. - Proven track record of independently delivering complex data pipelines or platform capabilities from problem definition through production. - Strong Python and SQL skills, including writing maintainable production code and optimizing non-trivial queries. - Hands-on experience with workflow orchestration tools such as Airflow, Prefect, or Dagster. - Solid grasp of data modeling, including designing maintainable analytical models and data contracts for multiple consumers. - Understanding of data architectures and storage systems, with awareness of trade-offs between processing and storage approaches. - Experience designing for reliability through testing, monitoring, data-quality validation, alerting, debugging, and incident resolution. - Ability to make technical decisions under ambiguity, explain trade-offs, and collaborate with both engineers and non-technical stakeholders.
Nice to have
- Experience with real-time or event-driven data platforms and streaming technologies. - Experience building or operating cloud-native services with Docker and Kubernetes. - Familiarity with Infrastructure as Code, especially Terraform. - Background in data governance, access control, privacy, or compliance frameworks such as GDPR or SOC 2. - Experience with data observability and quality tooling like Great Expectations. - History of raising engineering standards through shared libraries, platform tooling, documentation, or mentoring.
Benefits and work setup
- Competitive compensation package. - Career growth and structured learning opportunities. - Flexible working arrangements with meaningful ownership of work. - Collaborative, innovative engineering culture. - Opportunity to contribute to impactful AI infrastructure projects. - International environment with distributed teams across multiple regions.