Lead Data Engineer

What You Bring

• 8+ years of hands-on experience in data engineering, designing and delivering production-grade data platforms.
• Expert-level knowledge of Apache Spark, including deep understanding of runtime internals, performance tuning, and optimization to build scalable, high-performance pipelines.
• Proficient in writing clean, production-quality code in Python; hands-on Scala experience is a strong plus for advanced Spark and performance-critical workflows.
• Proven experience building and productionalizing data solutions on Databricks, including Delta Lake architectures, Unity Catalog governance, and Databricks Workflows. Databricks certification is a strong advantage.
• Hands-on expertise with at least two major cloud platforms (AWS, Azure, GCP) and in-depth experience with at least one, utilizing cloud-native services such as AWS Redshift, Glue, S3; Azure Synapse, Data Factory, ADLS; or Google BigQuery, Dataflow, GCS.
• End-to-end leadership of data engineering projects in client-facing or consulting environments, managing technical scope, stakeholder engagement, and timely delivery without compromising quality.
• Strong DataOps practice: CI/CD for data pipelines, automated testing, observability, and infrastructure-as-code are integral to your workflows.
• Broad experience in ETL/ELT design, data warehousing, lakehouse architectures, and data modeling, with the ability to select and apply the right approach for diverse scenarios.
• Excellent communicator, able to engage effectively with both technical and non-technical stakeholders, from C-level executives to junior engineers.

What You'll Do

Qualifications

• 8+ years of hands-on experience in data engineering, designing and delivering production-grade data platforms.
• Expert-level knowledge of Apache Spark, including deep understanding of runtime internals, performance tuning, and optimization to build scalable, high-performance pipelines.
• Proficient in writing clean, production-quality code in Python; hands-on Scala experience is a strong plus for advanced Spark and performance-critical workflows.
• Proven experience building and productionalizing data solutions on Databricks, including Delta Lake architectures, Unity Catalog governance, and Databricks Workflows. Databricks certification is a strong advantage.
• Hands-on expertise with at least two major cloud platforms (AWS, Azure, GCP) and in-depth experience with at least one, utilizing cloud-native services such as AWS Redshift, Glue, S3; Azure Synapse, Data Factory, ADLS; or Google BigQuery, Dataflow, GCS.
• End-to-end leadership of data engineering projects in client-facing or consulting environments, managing technical scope, stakeholder engagement, and timely delivery without compromising quality.
• Strong DataOps practice: CI/CD for data pipelines, automated testing, observability, and infrastructure-as-code are integral to your workflows.
• Broad experience in ETL/ELT design, data warehousing, lakehouse architectures, and data modeling, with the ability to select and apply the right approach for diverse scenarios.
• Excellent communicator, able to engage effectively with both technical and non-technical stakeholders, from C-level executives to junior engineers.
• What You'll Do
• As a Lead Data Engineer at Rearc, you’ll be the technical lead for complex, client-facing data engineering projects. You’ll own both architecture and implementation.
• Lead Client Data Engagements: Serve as the senior technical lead on client projects, owning the architecture, guiding the build, managing delivery risk, and ensuring the shipped solution aligns with client promises.
• Build and Productionize Data Solutions: Design, implement, and optimize scalable, reliable data pipelines and lakehouse architectures on Databricks and cloud platforms. Set the engineering standards for delivery through active coding and review.
• Architect for Scale and Reliability: Translate complex client requirements into robust technical solutions, reference architectures, and data models built for durability and maintainability.
• Drive Technical Delivery: Oversee technical scope and timelines, proactively surface blockers, and coordinate with project managers and stakeholders to keep engagements on track.
• Mentor Data Engineers: Coach junior and mid-level engineers through paired programming, code reviews, and constructive feedback.
• Promote Knowledge Sharing: Develop and contribute technical blogs, reference architectures, and internal guides based on practical client experiences.
• Champion DataOps Practices: Establish and uphold modern engineering standards, including automated testing, pipeline observability, version control, CI/CD, and comprehensive documentation.
• If you’re passionate about data engineering, committed to engineering excellence, and energized by solving challenging problems for clients, Rearc offers a high-impact role with significant opportunity for growth and leadership.
• 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.

Responsibilities

• As a Lead Data Engineer at Rearc, you’ll be the technical lead for complex, client-facing data engineering projects. You’ll own both architecture and implementation.
• Lead Client Data Engagements: Serve as the senior technical lead on client projects, owning the architecture, guiding the build, managing delivery risk, and ensuring the shipped solution aligns with client promises.
• Build and Productionize Data Solutions: Design, implement, and optimize scalable, reliable data pipelines and lakehouse architectures on Databricks and cloud platforms. Set the engineering standards for delivery through active coding and review.
• Architect for Scale and Reliability: Translate complex client requirements into robust technical solutions, reference architectures, and data models built for durability and maintainability.
• Drive Technical Delivery: Oversee technical scope and timelines, proactively surface blockers, and coordinate with project managers and stakeholders to keep engagements on track.
• Mentor Data Engineers: Coach junior and mid-level engineers through paired programming, code reviews, and constructive feedback.
• Promote Knowledge Sharing: Develop and contribute technical blogs, reference architectures, and internal guides based on practical client experiences.
• Champion DataOps Practices: Establish and uphold modern engineering standards, including automated testing, pipeline observability, version control, CI/CD, and comprehensive documentation.
• If you’re passionate about data engineering, committed to engineering excellence, and energized by solving challenging problems for clients, Rearc offers a high-impact role with significant opportunity for growth and leadership.
• 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.