Senior Staff Engineer - Data Platform

India·BangaloreFull-timesenior

Data EngineerData

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Quick Summary

Key Responsibilities

Own the end-to-end architecture and technical roadmap of the data platform, ensuring scalability, reliability, performance, and long-term maintainability. Databricks Engineering: Design, develop,

Requirements Summary

Own the end-to-end architecture and technical roadmap of the data platform, ensuring scalability, reliability, performance, and long-term maintainability. Databricks Engineering: Design, develop,

Technical Tools

Data EngineerData

Requirements

~1 min read

12–17 years of overall experience in software engineering, Data Engineering, or data platform development.
Significant experience building and scaling enterprise-grade data platforms in complex environments.
Proven track record of technical leadership, architectural ownership, and successful delivery of mission-critical data systems.
Excellent problem-solving, communication, stakeholder management, and mentoring skills.

Responsibilities

~1 min read

→Data Platform Architecture: Own the end-to-end architecture and technical roadmap of the data platform, ensuring scalability, reliability, performance, and long-term maintainability.
→Databricks Engineering: Design, develop, and optimize Databricks-based data solutions using Apache Spark, Delta Lake, and Lakehouse architecture principles.
→Data Pipeline Development: Build and maintain robust batch and streaming data pipelines to support analytics, reporting, machine learning, and operational use cases.
→Distributed Systems: Architect high-throughput, fault-tolerant data processing systems capable of handling large volumes of structured and unstructured data.
→Transactional Systems Integration: Design efficient data integration patterns between transactional databases, analytical platforms, and downstream data consumers.
→Performance Optimization: Identify and resolve bottlenecks in data processing, storage, query execution, and resource utilization to improve system efficiency and control infrastructure costs.
→Data Governance and Quality: Establish standards for data quality, lineage, security, access control, metadata management, and regulatory compliance.
→Technical Leadership: Lead complex engineering initiatives, influence architectural decisions across teams, and establish best practices for data engineering and platform development.
→Cross-Functional Collaboration: Partner with product managers, analytics teams, data scientists, and engineering leaders to translate business requirements into scalable technical solutions.
→Mentorship and Innovation: Mentor senior engineers, conduct architecture reviews, promote engineering excellence, and evaluate emerging technologies to continuously improve platform capabilities.

Extensive experience in Data Engineering, data platform architecture, and large-scale data processing.
Strong hands-on expertise in Databricks, Apache Spark, Delta Lake, and Lakehouse architecture.
Proven experience designing and operating scalable ETL/ELT pipelines and distributed data systems.
Strong understanding of data modeling, data warehousing, batch processing, and real-time data streaming.
Experience with cloud-based data platforms, preferably AWS, Microsoft Azure, or Google Cloud Platform.
Proficiency in SQL and at least one programming language, such as Python or Scala.
Strong knowledge of data security, governance, reliability, observability, and performance optimization.
Demonstrated ability to lead architecture initiatives and influence technical strategy across multiple engineering teams.