Lead Data Engineer (Databricks)

Rearc; Bengaluru; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Rearc is seeking a Lead Data Engineer located in Bengaluru, India. In this role, you will serve as a technical anchor on complex data engineering projects, collaborating directly with client teams to address their data challenges.

You will lead the design and implementation of scalable data solutions using your expertise in Databricks, Apache Spark, and cloud platforms. A strong background in DataOps is essential, along with excellent communication skills to engage diverse stakeholders effectively.

Qualifications

• 8+ years of hands-on data engineering experience with production-grade data platforms.
• Expert-level in Apache Spark, performance tuning, and optimization.
• Clean, production-quality code in Python; Scala experience is a strong plus.

Responsibilities

• Serve as the senior technical lead on client projects.
• Design and implement scalable, reliable data pipelines and lakehouse architectures.
• Translate complex client requirements into robust technical designs.

Skills

Apache Spark

DataOps

Python

ETL/ELT design

Cloud (AWS, Azure, GCP)

Databricks

Communication skills

Job description

As a Lead 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.

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 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 BigQuery/Dataflow/GCS.
• You’ve led 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.
• You bring a DataOps 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.

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.