Senior Data Engineer

In this vital role, 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 prefers 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 data sets.
• 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 DevOps 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 architecture.

Basic Qualifications

• Education and Experience:Master's degree and 4 to 6 years of Computer Science, IT, or related field experience; OR
• Bachelor's degree and 6 to 8 years of Computer Science, IT, or related field experience.
• Certifications (Preferred):AWS Certified Data Engineer
• Databricks Certificate
• Scaled Agile SAFe certification

Functional Skills

Must-Have Skills

• Hands-on experience in data engineering technologies such as Databricks, PySpark, SparkSQL, Apache Spark, AWS, Python, SQL, and Scaled Agile methodologies.
• Proficiency in workflow orchestration, 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 collaboration skills.
• Experience with Scaled Agile Framework (SAFe), Agile delivery practices, and DevOps 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/NOSQL database, vector database 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 DevOps.

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.