Senior Data Engineer (India)

About the Role

What You'll Do

What You Bring

• 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.
• 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.

Qualifications

• 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.
• 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

• 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.