Note: The job is a remote job and is open to candidates in USA. Rearc is a company focused on empowering engineers to build products and experiences through ownership, technical depth, and problem-solving. The Lead Data Engineer serves as the hands-on technical lead for complex, client-facing data engineering engagements, owning architecture, implementation, delivery, mentoring, and DataOps practices.
Responsibilities
- Lead Client Data Engagements — Serve as the senior technical lead on client projects. Own the architecture, guide the build, manage delivery risk, and ensure the solution shipped matches what was promised
- Build and Productionize Data Solutions — Design and implement scalable, reliable data pipelines and lakehouse architectures on Databricks and cloud platforms. You're hands-on keyboard — you write code, review code, and set the engineering standard for the engagement
- Architect for Scale and Reliability — Translate complex client requirements into robust technical designs, reference architectures, and data models built to last in production
- Drive Technical Delivery — Manage technical scope and timelines, identify blockers early, and partner with project managers and client stakeholders to keep engagements on track
- Mentor Data Engineers — Coach junior and mid-level engineers through hands-on pairing, code review, and direct feedback, raising the floor for everyone around you
- Promote Knowledge Sharing — Contribute technical blogs, reference architectures, and internal guides that reflect hard-won lessons from real client work
- Champion DataOps Practices — Establish and enforce modern data engineering standards across engagements: automated testing, pipeline observability, version control, CI/CD, and documentation
Skills
- • 8+ years of hands-on data engineering experience, designing and delivering production-grade data platforms
- • Expert-level in Apache Spark, including runtime internals, performance tuning, and optimization — you understand what's happening under the hood and use that knowledge to build pipelines that perform at scale
- • Clean, production-quality code in Python, with Scala experience a strong plus for deeper Spark and performance-critical work
- • Experience building and productionizing solutions on Databricks, including Delta Lake architectures, Unity Catalog governance, and Databricks Workflows
- • Real, working experience across at least two major cloud platforms (AWS, Azure, GCP), with genuine depth in at least one — including cloud-native services such as AWS Redshift/Glue/S3, Azure Synapse/Data Factory/ADLS, or Google BigQuery/Dataflow/GCS
- • A track record leading data engineering projects end-to-end in a client-facing or consulting context, managing technical scope, navigating stakeholder expectations, and delivering against timelines without cutting corners
- • A DataOps mindset — CI/CD for data pipelines, automated testing, observability, and infrastructure-as-code are standard practice for you, not afterthoughts
- • Experience spanning ETL/ELT design, data warehousing, lakehouse architecture, and data modeling, and the judgment to know when to apply each approach
- • Strong communication skills that let you engage technical and non-technical stakeholders equally well — from a client's CTO to a junior engineer on your team
- Scala experience a strong plus for deeper Spark and performance-critical work
- Databricks certification is a strong plus
Company Overview
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