This role will be based in Bangalore, India.
At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team.
Team Blurb: Talent Productivity Engineering (TPE) at LinkedIn builds enterprise platforms, applications, and data solutions that power critical business functions, including Talent Acquisition, Human Resources, Finance, and Benefits.
Our team designs and operates secure, scalable, and reliable data platforms that integrate enterprise systems, enable workforce analytics, and support data driven business decisions. We leverage modern cloud technologies, data engineering practices, and governance capabilities to deliver trusted, high-quality data products across LinkedIn.
As a Senior Software Engineer, you will play a pivotal role in designing, developing, and modernizing enterprise Data Warehouse and Lakehouse platforms. You will work with Azure cloud technologies, distributed data processing frameworks, and enterprise integrations to deliver scalable, secure, and high-performing data solutions.
You will make key architectural decisions, solve complex engineering challenges, collaborate with cross-functional teams, and drive engineering excellence through innovation, automation, and technical mentorship.
Responsibilities
Design, develop, and maintain scalable enterprise Data Warehouse and Lakehouse solutions using Azure Databricks, Azure Data Factory, Azure Data Lake Storage, and SQL Server.
Build and optimize batch and incremental data pipelines using Python, PySpark, SQL, and distributed data processing frameworks.
Develop secure and reliable data integrations with enterprise applications using REST/SOAP APIs, SFTP, and database connectivity.
Implement data modeling, data quality, metadata management, lineage, and governance capabilities to deliver trusted, analytics-ready datasets.
Design and maintain highly available, secure, and performant data platforms with monitoring, automation, and disaster recovery capabilities.
Drive engineering excellence through architectural decisions, CI/CD, automated testing, performance optimization, and technical mentorship.
Basic Qualifications
Bachelor's degree in Computer Science or a related technical discipline, or equivalent practical experience.
5+ years of experience designing and developing enterprise data warehouses, data pipelines, or distributed data processing solutions.
Hands-on programming experience with Python, PySpark, and SQL.
Experience with cloud-based data platforms, ETL/ELT frameworks, and relational databases.
Experience designing and developing backend services, APIs, or applications using modern software engineering principles.
Understanding of data modeling, distributed systems, software architecture, data structures, algorithms, and production engineering practices.
Preferred Qualifications
Experience with Azure Data Factory, Azure Databricks, Azure Data Lake Storage Gen2, and Delta Lake.
Experience with Microsoft SQL Server, including T-SQL, performance tuning, and high-availability architectures.
Experience integrating enterprise applications through REST/SOAP APIs, SFTP, and secure data connectivity.
Experience developing Python-based services and APIs using frameworks such as FastAPI or Flask.
Familiarity with modern application architectures, microservices, asynchronous processing, and event-driven systems.
Familiarity with LLMs, embeddings, RAG architectures, vector search, and enterprise AI application development.
Knowledge of data governance, metadata management, data lineage, and data quality frameworks.
Experience with Azure security, CI/CD, automated testing, monitoring, and production support.
Familiarity with Azure Analysis Services, Power BI, and real-time data processing technologies.
Suggested Skills
Data Engineering: Python, PySpark, SQL, Apache Spark
Azure Data Platform: Databricks, Data Factory, ADLS Gen2, SQL Server
Data Architecture: Data Warehousing, ETL/ELT, Data Modeling, Data Governance
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