rearc - Senior Data Engineer
Requirements
• 6+ 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. Databricks certification is a strong plus. • 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 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.
Responsibilities
• Contribute to Client Data Engagements — Serve as a senior technical contributor on client projects. Contribute to the architecture, guide the build, and help 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. • Support Technical Delivery — Support 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.
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