Staff Engineer, Big Data

Company Description 👋🏼We're Nagarro. We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale across all devices and digital mediums, and our people exist everywhere in the world (18000+ experts across 39 countries, to be exact). Our work culture is dynamic and non-hierarchical. We're looking for great new colleagues. That's where you come in Job Description REQUIREMENTS: Total experience: 5.5+ years. Strong hands-on experience in Python and SQL programming. Must-have expertise in Databricks, Apache Spark, Apache Kafka, and Terraform. Strong experience building scalable batch and real-time data pipelines on Azure Databricks or similar cloud platforms. Hands-on experience with ETL/ELT development, data ingestion, transformation, and orchestration. Experience working with streaming technologies such as Apache Kafka or Azure Event Hub. Good understanding of Delta Lake, Unity Catalog, and Medallion Architecture (Raw, Trusted, Curated). Experience with Databricks Workflows, Airflow, or similar workflow orchestration tools. Strong knowledge of CI/CD, Git, GitHub Actions, and Infrastructure as Code using Terraform. Experience developing high-quality, scalable, and maintainable Python, SQL, and Spark code. Good understanding of cloud-based data platforms, preferably Azure Databricks (GCP BigQuery or equivalent is acceptable). Experience with metadata-driven data ingestion frameworks and automated pipeline development. Familiarity with dbt and modern data engineering best practices. Working knowledge of Kubernetes is an added advantage. Exposure to Cybersecurity data domains (SIEM, EDR, Cloud Security Logs, OCSF) is desirable. Experience with Cribl or log routing/observability pipelines is a plus. Exposure to Machine Learning, AI, or GenAI solutions on Databricks is preferred. Strong understanding of Agile development methodologies and DevOps practices. Excellent analytical, problem-solving, communication, and stakeholder management skills. RESPONSIBILITIES: Design, develop, and maintain scalable batch, near real-time, and streaming data pipelines using Databricks, Apache Spark, and Python. Build and operationalize end-to-end data ingestion pipelines from multiple data sources into the enterprise data lake. Develop data transformation frameworks supporting Raw, Trusted, and Curated data layers. Contribute to the design and implementation of scalable cloud-based data architectures. Develop high-quality Python, SQL, and Spark code while participating in peer code reviews. Build and optimize ETL/ELT pipelines following metadata-driven engineering standards. Implement and maintain streaming data integrations using Apache Kafka and related technologies. Automate deployment, configuration, and infrastructure provisioning using Terraform, GitHub Actions, and CI/CD pipelines. Collaborate closely with Platform Engineering, Operations, Security, and Business teams to deliver reliable data solutions. Monitor, troubleshoot, and optimize data pipelines to ensure high availability and performance. Develop operational runbooks and support production incident analysis and resolution. Ensure data quality, governance, and compliance across cloud data platforms. Contribute to engineering best practices, coding standards, documentation, and knowledge sharing. Mentor junior engineers and support continuous improvement initiatives across the data engineering team. Evaluate and adopt modern cloud technologies, Databricks capabilities, and AI/ML innovations to enhance the data platform. Qualifications Bachelor’s or master’s degree in computer science, Information Technology, or a related field