Architect (Data Engineering)

Roles & Responsibilities:

Design and implement scalable, modular, and future-proof data architectures that initiatives in enterprise.

Develop enterprise-wide data frameworks that enable governed, secure, and accessible data across various business domains.

Define data modeling strategies to support structured and unstructured data, ensuring efficiency, consistency, and usability across analytical platforms.

Lead the development of high-performance data pipelines for batch and real-time data processing, integrating APIs, streaming sources, transactional systems, and external data platforms.

Optimize query performance, indexing, caching, and storage strategies to enhance scalability, cost efficiency, and analytical capabilities.

Establish data interoperability frameworks that enable seamless integration across multiple data sources and platforms.

Drive data governance strategies, ensuring security, compliance, access controls, and lineage tracking are embedded into enterprise data solutions.

Implement DataOps best practices, including CI/CD for data pipelines, automated monitoring, and proactive issue resolution, to improve operational efficiency.

Lead Scaled Agile (SAFe) practices, facilitating Program Increment (PI) Planning, Sprint Planning, and Agile ceremonies, ensuring iterative delivery of enterprise data capabilities.

Collaborate with business stakeholders, product teams, and technology leaders to align data architecture strategies with organizational goals.

Act as a trusted advisor on emerging data technologies and trends, ensuring that the enterprise adopts cutting-edge data solutions that provide competitive advantage and long-term scalability.

Must-Have Skills:

Experience in data architecture, enterprise data management, and cloud-based analytics solutions.

Well versed in domain of Biotech/Pharma industry and has been instrumental in solving complex problems for them using data strategy.

Expertise in Databricks, cloud-native data platforms, and distributed computing frameworks.

Strong proficiency in modern data modeling techniques, including dimensional modeling, NoSQL, and data virtualization.

Experience designing high-performance ETL/ELT pipelines and real-time data processing solutions.

Deep understanding of data governance, security, metadata management, and access control frameworks.

Hands-on experience with CI/CD for data solutions, DataOps automation, and infrastructure as code (IaC).

Proven ability to collaborate with cross-functional teams, including business executives, data engineers, and analytics teams, to drive successful data initiatives.

Strong problem-solving, strategic thinking, and technical leadership skills.

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 Apache Spark, Apache Airflow

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 DevOps

Good-to-Have Skills:

Experience with Data Mesh architectures and federated data governance models.

Certification in cloud data platforms or enterprise architecture frameworks.

Knowledge of AI/ML pipeline integration within enterprise data architectures.

Familiarity with BI & analytics platforms for enabling self-service analytics and enterprise reporting.

Education and Professional Certifications

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

AWS Certified Data Engineer preferred

Databricks Certificate preferred