Job description

At Rearc, we're committed to empowering engineers to build awesome products and experiences. Success as a business hinges on our people's ability to think freely, challenge the status quo, and speak up about alternative problem-solving approaches. If you're driven by the desire to solve problems and make a difference, you're in the right place! Our approach is simple: empower our people with the best tools possible to make an impact within their industry. We're on the lookout for people who thrive on ownership and freedom, possessing not just technical depth but also executive presence and business judgment. Founded in 2016, we pride ourselves on fostering an environment where creativity flourishes, bureaucracy is minimal, and individuals are encouraged to challenge the status quo. We're not just a company; we're a community of problem-solvers dedicated to improving the lives of fellow software engineers and the customers we serve. ABOUT THE ROLE As a Lead Data Engineer at Rearc, you'll serve as the hands-on technical lead across complex, client-facing data engineering engagements. You'll own the architecture and the build — not just guide it from a distance. WHAT YOU'LL DO - 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. WHAT YOU BRING - 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. 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 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. Rearc is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees, regardless of race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. All employment decisions at Rearc are based on business needs, job requirements, and individual qualifications.

Requirements

Must-haves

As a Lead Data Engineer at Rearc, you'll serve as the hands-on technical lead across complex, client-facing data engineering engagements. You'll own the architecture and the build — not just guide it from a distance.
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.
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.
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. Databricks certification is a strong plus.
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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

ci/cdpythonawsazuregcp

Platform: ashby
Effort: quick