Description

**Job Description**

ABOUT THE ROLE

Role Description:

Let’s do this. Let’s change the world. We are looking for highly motivated expert Principal Data Engineer who can own the design & development of complex data pipelines, solutions and frameworks. 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:

+ Architect and maintain robust, scalable data pipelines using Databricks, Spark, and Delta Lake, enabling efficient batch and real-time processing. 

+ Lead efforts to evaluate, adopt, and integrate emerging technologies and tools that enhance productivity, scalability, and data delivery capabilities. 

+ Drive performance optimization efforts, including Spark tuning, resource utilization, job scheduling, and query improvements. 

+ Identify and implement innovative solutions that streamline data ingestion, transformation, lineage tracking, and platform observability. 

+ Build frameworks for metadata-driven data engineering, enabling reusability and consistency across pipelines. 

+ Foster a culture of technical excellence, experimentation, and continuous improvement within the data engineering team. 

+ Collaborate with platform, architecture, analytics, and governance teams to align platform enhancements with enterprise data strategy. 

+ Define and uphold SLOs, monitoring standards, and data quality KPIs for production pipelines and infrastructure. 

+ Partner with cross-functional teams to translate business needs into scalable, governed data products. 

+ Mentor engineers across the team, promoting knowledge sharing and adoption of modern engineering patterns and tools. 

+ 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

+ 12 to 17 years of experience in Computer Science, IT or related field 

+ 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.