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We are looking for an experienced Databricks Lead Developer / Architect with 7+ years of experience in data engineering, cloud data platforms, and distributed data processing. The ideal candidate will have strong hands-on expertise with Databricks, Apache Spark, Python, SQL, and modern data lake/lakehouse architectures, with the ability to lead technical design and development initiatives. Experience working with AWS cloud services and a Databricks certification will be an added advantage. Preferred Location: Mohali/Noida Key Responsibilities: Design and architect scalable, secure, and high-performance Databricks/Lakehouse solutions. Lead the development of enterprise-grade data engineering pipelines using Databricks, Apache Spark, Python, and SQL. Define technical architecture, development standards, best practices, and reusable frameworks. Design and implement data ingestion, transformation, integration, and processing pipelines. Work extensively with Delta Lake and develop optimized data models and lakehouse architectures. Optimize Spark and Databricks workloads for performance, scalability, reliability, and cost. Provide technical leadership and mentoring to data engineers and development teams. Collaborate with business stakeholders, data architects, analysts, and engineering teams to translate requirements into technical solutions. Implement data quality, validation, monitoring, logging, and error-handling frameworks. Design solutions for batch and, where applicable, streaming data processing. Establish CI/CD and deployment practices for Databricks workloads. Ensure solutions follow enterprise standards for security, governance, access control, and data management. Troubleshoot complex production issues and provide technical guidance to development teams. Evaluate new Databricks and cloud capabilities and recommend improvements to the data platform. Skills & Experience: 7+ years of overall experience in data engineering, software development, or data platform engineering. Strong hands-on experience with Databricks and Apache Spark. Strong programming experience in Python and SQL. Solid understanding of Delta Lake, Lakehouse architecture, data warehousing, and data lake concepts. Experience designing and developing scalable ETL/ELT data pipelines. Strong understanding of Spark architecture, optimization, partitioning, caching, joins, and performance tuning. Experience with data integration and processing of structured and semi-structured data. Experience with Databricks Workflows/Jobs, notebooks, clusters, and deployment processes. Good understanding of data modeling and distributed data processing. Experience with version control and CI/CD practices, preferably using Git and automated deployment pipelines. Strong problem-solving, analytical, communication, and technical leadership skills. Ability to independently own architecture and technical decisions for complex data engineering solutions. AWS Experience Preferred Experience working with AWS-based data platforms is highly desirable, including one or more of: Amazon S3 AWS Glue AWS Lambda Amazon Redshift Amazon EMR AWS IAM Amazon CloudWatch AWS networking and security concepts Experience integrating Databricks with AWS services and designing cloud-native data architectures will be an added advantage. Preferred / Nice-to-Have Databricks certification such as Databricks Certified Data Engineer or Databricks Certified Data Engineer Qualified. Experience with Delta Live Tables / Lakeflow Declarative Pipelines. Experience with Unity Catalog and Databricks governance. Experience with Databricks SQL and BI/data analytics integrations. Experience with real-time/streaming technologies such as Kafka or Spark Structured Streaming. Experience with orchestration tools such as Airflow or similar platforms. Knowledge of infrastructure-as-code tools such as Terraform. Experience implementing enterprise data governance, security, and observability. Experience working in Agile/Scrum development environments. Leadership Expectations Act as the technical lead and subject-matter expert for Databricks and data engineering initiatives. Review technical designs and code and establish engineering best practices. Mentor developers and help build technical capability within the team. Drive architecture decisions and provide guidance on scalability, performance, security, and maintainability. Communicate complex technical concepts effectively to both technical and non-technical stakeholders. Education : Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field is preferred. Experience Level: 7+ years of overall experience, with significant hands-on experience in Databricks and data engineering and preferably experience in AWS cloud environments. .
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