Senior Data Engineer

As a Senior Data Engineer at Rearc, you'll be the technical anchor on complex, client-facing data engineering engagements, someone who can sit across the table from a client's data leadership team, understand their most difficult data challenges, and then go head-down and build a solution. You'll bring deep, hands‑on expertise with Databricks and the broader modern data stack, and you'll set the technical standard for how we design, build, and deliver data platforms that actually work in production.

You'll write code, build pipelines, and architect solutions side‑by‑side with your team and your clients.

• 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 optimisation, you understand what's happening under the hood and use that knowledge to build pipelines that perform at scale.
• You write clean, production‑quality code in Python, with Scala experience a strong plus for deeper Spark and performance‑critical work.
• You've built and productionalized solutions on the platform, including Delta Lake architectures, Unity Catalog governance, and Databricks Workflows. Databricks certification is a strong plus.
• You have 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 Big Query/Dataflow/GCS.
• You bring a Data Ops mindset: CI/CD for data pipelines, automated testing, observability, and infrastructure‑as‑code are standard practice for you, not afterthoughts.
• Your experience spans ETL/ELT design, data warehousing, lakehouse architecture, and data modelling, and you know when to apply each approach.
• Your communication skills allow you to engage technical and non‑technical stakeholders equally well, from a client's CTO to a junior engineer on your team.

• 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 Data Ops Practices :
  Establish and enforce modern data engineering standards across engagements, automated testing, pipeline observability, version control, CI/CD, and documentation.

The base salary range for this role is $160,000 - $200,000 USD per year. Compensation may vary based on skills, experience, and location.

160, USD per year (Hybrid (New York, New York, US))