Senior Data Engineer

Location: Prakāshamnagar

We are looking for a highly motivated and expert Senior Data Engineer who can own the design & development of complex data pipelines, solutions, and frameworks. The ideal candidate will be responsible for designing, developing, and optimizing data pipelines, data integration frameworks, and metadata-driven architectures that enable seamless data access and analytics. This role requires deep expertise in big data processing, distributed computing, data modeling, and governance frameworks to support self-service analytics, AI-driven insights, and enterprise-wide data management.

Roles & Responsibilities    Design, develop, and maintain scalable ETL/ELT pipelines to support structured, semi-structured, and unstructured data processing across the Enterprise Data Fabric.
Implement real-time and batch data processing solutions, integrating data from multiple sources into a unified, governed data fabric architecture.
Optimize big data processing frameworks using Apache Spark, Hadoop, or similar distributed computing technologies to ensure high availability and cost efficiency.
Work with metadata management and data lineage tracking tools to enable enterprise-wide data discovery and governance.
Ensure data security, compliance, and role-based access control (RBAC) across data environments.
Optimize query performance, indexing strategies, partitioning, and caching for large-scale datasets.
Develop CI/CD pipelines for automated data pipeline deployments, version control, and monitoring.
Implement data virtualization techniques to provide seamless access to data across multiple storage systems.
Collaborate with cross-functional teams, including data architects, business analysts, and Dev Ops teams, to align data engineering strategies with enterprise goals.
Stay up-to-date with emerging data technologies and best practices, ensuring continuous improvement of Enterprise Data Fabric architectures.
Functional Skills   Must-Have Skills   Hands-on experience in data engineering technologies such as Databricks, PySpark, Spark

SQL, Apache Spark, AWS, Python, SQL, and Scaled Agile methodologies.
Proficiency in workflow orchestration and performance tuning on big data processing.
Strong understanding of AWS services.

Experience with Data Fabric, Data Mesh, or similar enterprise-wide data architectures.
Ability to quickly learn, adapt, and apply new technologies.
Strong problem-solving and analytical skills.
Excellent communication and teamwork skills.

Experience with Scaled Agile Framework (SAFe), Agile delivery practices, and Dev Ops practices.
Good-to-Have Skills   Deep expertise in Biotech & Pharma industries.
Experience in writing APIs to make the data available to the consumers.
Experienced with SQL/No

SQL databases, and vector databases for large language models.
Experienced with data modeling and performance tuning for both OLAP and OLTP databases.
Experienced with software engineering best-practices, including but not limited to version control (Git, Subversion, etc.), CI/CD (Jenkins, Maven, etc.), automated unit testing, and Dev Ops.
Education and Professional Certifications    9 to 12 years of Computer Science, IT, or related field experience.
AWS Certified Data Engineer (Preferred).
Databricks Certificate (Preferred).
Scaled Agile SAFe certification (Preferred).
Soft Skills    Excellent analytical and troubleshooting skills.
Strong verbal and written communication skills.
Ability to work effectively with global, virtual teams.
High degree of initiative and self-motivation.
Ability to manage multiple priorities successfully.
Team-oriented, with a focus on achieving team goals.
Ability to learn quickly, be organized, and detail-oriented.
Strong presentation and public speaking skills.