Senior Databricks Engineer

Keka Technologies Private Limited; Hyderabad; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Keka Technologies Private Limited is looking for an experienced Senior Databricks Developer in Hyderabad, India. The ideal candidate will have over 8 years of expertise in Data Engineering and Cloud Analytics, leading projects and mentoring teams. Key responsibilities include collaborating with stakeholders, designing data solutions, and implementing best practices in coding and architecture.

This role offers a chance to work with advanced technologies like Databricks, Apache Spark, and cloud platforms like Azure and AWS.

Qualifications

• 8+ years of experience in Data Engineering, Big Data, and Analytics platforms.
• Minimum 5+ years of hands-on experience working with Databricks, Apache Spark, PySpark, and SQL.
• Strong expertise in designing and implementing enterprise-scale Data Lake, Lakehouse, and Data Warehouse solutions.

Responsibilities

• Lead the design, development, and implementation of enterprise-scale data engineering solutions using Databricks.
• Collaborate with clients and stakeholders to understand business requirements.
• Drive data transformation initiatives from requirements gathering through deployment.

Skills

Databricks

Apache Spark

PySpark

SQL

Delta Lake

Data Engineering

Big Data

Cloud Analytics

Education

Bachelor's or Master's degree in Computer Science, Engineering, Information Technology

Tools

Azure Data Factory (ADF)

AWS S3

Azure Synapse Analytics

Databricks Job Clusters

Power BI

Tableau

Job description

We are seeking an experienced Senior Databricks Developer with 8+ years of experience in Data Engineering, Big Data, and Cloud Analytics solutions, including extensive hands‑on expertise in Databricks, Apache Spark, PySpark, SQL, and Delta Lake. The ideal candidate will be responsible for leading end‑to‑end data engineering projects, engaging directly with clients and business stakeholders, mentoring development teams, and delivering scalable, secure, and high‑performance data platforms across Azure and AWS environments.

This role requires strong technical leadership, solution design capabilities, stakeholder management skills, and the ability to drive data transformation initiatives from requirements gathering through deployment and production support.

Technical Expertise

• Data Factory (ADF)
• Azure Synapse Analytics
• AWS S3
• Glue
• Lambda
• Unity Catalog
• MLflow
• Databricks Job Clusters
• CI/CD Pipelines
• GitLab / GitHub
• Data Governance & Data Quality Frameworks
• Power BI / Tableau
• Data Cataloging
• Lakehouse Architecture
• Medallion Architecture
• Performance Tuning & Optimization

Key Responsibilities

• Lead the design, development, and implementation of enterprise‑scale data engineering solutions using Databricks, PySpark, SQL, and Delta Lake as well as development, testing, deployment, and production support.
• Collaborate directly with clients, business stakeholders, architects, and product owners to understand business requirements and translate them into scalable technical solutions.
• Conduct client discussions, solution workshops, effort estimations, technical presentations, and architecture reviews.
• Design and implement scalable Lakehouse architectures and data platforms across Azure and AWS cloud environments.
• Lead the development of high‑performance ETL/ELT pipelines supporting both batch and continuous processes.
• Drive best practices around coding standards, architecture governance, version control, CI/CD implementation, and operational excellence.
• Architect and optimize Databricks Workflows, Job Clusters, Delta Tables, and Spark applications to maximize performance and minimize infrastructure costs.
• Implement data governance frameworks using Unity Catalog, ensuring proper access controls, lineage tracking, metadata management, and compliance standards.
• Mentor and guide junior and mid‑level data engineers through code reviews, technical coaching, and knowledge‑sharing initiatives.
• Lead technical teams and coordinate project activities to ensure timely and successful project delivery.
• Collaborate with Data Scientists, BI Teams, and Analytics stakeholders to enable advanced analytics and machine learning use cases.
• Drive automation initiatives through Infrastructure as Code (IaC), DevOps practices.
• Establish monitoring, observability, and performance tracking frameworks for data platforms and pipelines.
• Ensure security, compliance, data quality, and operational reliability across enterprise data ecosystems.
• Prepare and maintain technical architecture documents, design specifications, implementation guides, and operational runbooks.

Requirements

• 8+ years of experience in Data Engineering, Big Data, and Analytics platforms.
• Minimum 5+ years of hands‑on experience working with Databricks, Apache Spark, PySpark, and SQL.
• Strong expertise in designing and implementing enterprise‑scale Data Lake, Lakehouse, and Data Warehouse solutions.
• Deep understanding of Spark internals, cluster management, performance tuning, partitioning strategies, and resource optimization.
• Extensive experience working with Delta Lake, schema evolution, ACID transactions.
• Hands‑on experience integrating Databricks with Azure (ADF, Synapse) and/or AWS (S3, Glue, Lambda) services.
• Proven experience handling end‑to‑end project delivery and managing technical engagements with clients and stakeholders.
• Experience leading development teams and mentoring engineers in enterprise environments.
• Strong understanding of data modeling, dimensional modeling, Medallion Architecture.
• Expertise in implementing CI/CD pipelines, DevOps practices, and automated deployment strategies.
• Experience with Unity Catalog or equivalent governance platforms.
• Excellent communication, stakeholder management, presentation, and client‑facing skills.
• Ability to lead technical discussions, architecture reviews, and solution design workshops.
• Bachelor's or Master's degree in Computer Science, Engineering, Information Technology,