Note: The job is a remote job and is open to candidates in USA. GlobalLogic is a digital engineering company that helps brands design and build innovative products, platforms, and digital experiences. The Senior Data Engineer will build and improve production data pipelines, testing and deployment automation, data models, and AI/LLM engineering practices while establishing repeatable patterns and documentation for teams.
Responsibilities
- Production Python on Spark/Databricks: has shipped data pipelines as real software (importable modules, packaging, code review), with Delta/medallion and Databricks Asset Bundles familiarity
- Test automation and CI/CD: pytest and coverage wired into GitHub Actions as an actual deploy gate, plus dependency pinning and lockfiles. Bonus if they've made "no green tests, no deploy" stick on a team that wasn't already doing it
- Data engineering and data modeling: schema/data contracts, medallion boundaries, idempotent reprocessing, versioning, and lineage. Can deliver a blunt architecture critique backed by a prioritized backlog, not just an opinion
- Some AI/LLM engineering experience: token and cost instrumentation, batching and concurrency control, model selection and serving. An engineer who's worked around LLMs, not a research data scientist
- Enablement mindset: leaves a repeatable pattern and a written playbook so the team can keep going after they roll off. Comfortable in a fast moving, messy codebase rather than needing greenfield
Skills
- Production Python on Spark/Databricks: has shipped data pipelines as real software (importable modules, packaging, code review), with Delta/medallion and Databricks Asset Bundles familiarity
- Test automation and CI/CD: pytest and coverage wired into GitHub Actions as an actual deploy gate, plus dependency pinning and lockfiles
- Data engineering and data modeling: schema/data contracts, medallion boundaries, idempotent reprocessing, versioning, and lineage
- Can deliver a blunt architecture critique backed by a prioritized backlog, not just an opinion
- Some AI/LLM engineering experience: token and cost instrumentation, batching and concurrency control, model selection and serving
- An engineer who's worked around LLMs, not a research data scientist
- Enablement mindset: leaves a repeatable pattern and a written playbook so the team can keep going after they roll off
- Comfortable in a fast moving, messy codebase rather than needing greenfield
- Bachelor's or Master's Degree in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field
- Bonus if they've made "no green tests, no deploy" stick on a team that wasn't already doing it
Benefits
- Exciting projects using the latest technologies and working on market-defining products
- Collaborative environment with a diverse team in an open, laidback environment, including opportunities to work abroad in global centers or client facilities
- Flexible work schedules and opportunities to work from home
- Continuing education classes, professional certification, and training in technical, soft skills, language, and communication skills
- Additional learning opportunities through international knowledge exchanges
- Health and life insurance
- Short-term and long-term disability insurance
- Flexible spending accounts
- PTO and holidays
Company Overview
Company H1B Sponsorship