Sr Data Engineer
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 RD.
- 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
- Masters degree and 3 to 4 + years of relevant Computer Science, IT or related field experience OR
- Bachelors degree and 5 to 8 + years of relevant 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.
Skills
- Agile
- AI
- Analytics
- API
- AWS
- Big Data
- CI/CD
- Data Engineering
- Data Lineage
- Data Mesh
- Data Modeling
- Data Pipelines
- Databricks
- DevOps
- Distributed Computing
- ELT
- ETL
- Git
- Hadoop
- Jenkins
- LLM
- Maven
- Metadata Management
- NoSQL
- OLAP
- PySpark
- Python
- RBAC
- SAFe Agile
- Spark
- SQL
- Unit Testing
- Vector Databases
- Version Control
- Virtualization
- Workflow Orchestration