Career Category
Engineering
Job Description
Senior Data Engineer:
Roles & Responsibilities:
- Design, develop, and maintain scalable databricks 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 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:
- Develop pipelines using Databricks (Delta Lake, Spark, notebooks)
- Hands-on experience in data engineering technologies such as Databricks PySpark, SQL, and Scaled Agile methodologies.
- Proficiency in workflow orchestration, performance tuning on big data processing.
- Strong Programming skills and lead teams with technical acumen
- 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.
Preferred / Strategic Skills (Aligned to Future Data Strategy)
- Certification:
- Relevant certifications in Databricks, cloud platforms (AWS/Azure/GCP), or modern data engineering technologies are a plus
- Experience with:
- Delta Lake / Lakehouse architectures
- Data Fabric / Data Mesh concepts
- Familiarity with:
- Streaming data (Kafka, event-driven pipelines)
- Data orchestration tools (Airflow, Databricks Workflows)
- Exposure to:
- AI/ML data pipelines and feature engineering
- Unstructured data processing
- Understanding of:
- Data governance frameworks and cataloging tools
- Security and privacy controls for sensitive data
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
- 8 to 12 years of Computer Science, IT or related field experience
- Databricks Certificate 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.
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