Staff Data Engineer in India at Jobgether

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Staff Data Engineer in India.

In this role, you will take ownership of designing and scaling high-performance data architectures that power advanced analytics and customer experience insights at enterprise level. You will work on large-scale data pipelines, ensuring efficient ingestion, transformation, and delivery of structured and unstructured data across cloud-native environments. The position requires strong expertise in Databricks and modern data engineering practices to build reliable, scalable, and optimized systems. You will collaborate closely with data scientists, product managers, and business stakeholders to translate analytical needs into robust technical solutions. This is a senior individual contributor role with strong architectural influence and mentoring responsibilities. It offers the opportunity to shape core data infrastructure supporting mission-critical analytics platforms.

• Design and own scalable data architectures for ingestion, transformation, and analytics using Databricks and modern cloud platforms.
• Build and maintain robust ETL/ELT pipelines using PySpark, SQL, and Databricks Workflows.
• Optimize large-scale distributed data systems through performance tuning, partitioning, and efficient processing strategies.
• Develop and manage cloud-based data lakes and warehouses across platforms such as Azure, AWS, or GCP.
• Implement orchestration workflows using tools like Azure Data Factory, AWS Glue, or equivalent services.
• Integrate and unify structured, semi-structured, and unstructured data sources for analytics use cases.
• Establish frameworks for data quality, validation, monitoring, and observability across pipelines.
• Implement CI/CD pipelines for data workflows using Git-based systems and DevOps tools.
• Collaborate with analytics, BI, and data science teams to deliver production-ready datasets.
• Mentor junior engineers and promote engineering best practices across data development lifecycle.

• 8+ years of experience in data engineering, with at least 3 years in a senior or staff-level role.
• Strong hands-on expertise in Databricks (mandatory), including Spark, Delta Lake, and cluster optimization.
• Advanced proficiency in Python, SQL, and PySpark for large-scale data processing.
• Experience designing and implementing scalable ETL/ELT pipelines and data lake/warehouse architectures.
• Strong knowledge of cloud platforms such as Azure, AWS, or GCP (Azure preferred).
• Experience with CI/CD pipelines, Git, and data deployment automation tools.
• Familiarity with data governance, lineage, and observability tools (e.g., Great Expectations, Monte Carlo).
• Exposure to streaming technologies such as Kafka or Event Hubs is a plus.
• Strong problem-solving, analytical thinking, and communication skills.
• Ability to mentor engineers and collaborate effectively with cross-functional teams.

• Competitive compensation aligned with senior engineering roles.
• Hybrid work model with initial on-site collaboration followed by flexible remote work options.
• Opportunity to work on enterprise-scale data platforms and advanced analytics systems.
• Exposure to modern cloud and Databricks-driven data ecosystems.
• Strong career growth in a high-impact, senior technical individual contributor role.
• Collaborative environment working with global data, analytics, and product teams.
• Opportunity to influence data architecture and engineering standards at scale.