Senior Data Engineer

Role Description:

Let’s do this. Let’s change the world. We are looking for highly motivated expert Senior Data Engineer who can own the design & development of complex data pipelines, solutions and frameworks with detailed functional knowledge of R&D. The ideal candidate will be responsible to design, develop, and optimize 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 Engineering for Biotech or Pharma functional knowledge of R&D.

• 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 architectures.

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 teamwork skills

• Experience with Scaled Agile Framework (SAFe), Agile delivery practices, and DevOps practices.

Good-to-Have Skills:

• Good to have 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 Dev Ops

Education and Professional Certifications

• Master’s degree and 6 to 8 + years of Computer Science, IT or related field experience
  OR

• Bachelor’s degree and 7 to 10 + 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.