Senior Data Engineer

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.
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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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