Description

Key Skills: Data architecture, Data Modelling, AWS, Cloud (AWS / Azure / GCP), Azure, GCP, ETL

Roles and Responsibilities:

Data Landscape Assessment & Rationalization

• Assess current-state data ecosystems, including data sources, pipelines, and storage platforms.
• Identify redundancies, data silos, and inefficiencies, and define target-state data architecture and transformation roadmap.

Technology Evaluation

• Evaluate and recommend data platforms and tools (data lakes, lakehouse, ETL/ELT, streaming, cataloging, analytics) aligned to business needs.
• Conduct proof of concepts (PoCs) and vendor assessments for data technologies.

Data Architecture Design

• Define end-to-end Data Lake architecture (ingestion, storage, processing, semantic/consumption layers).
• Establish scalable, reusable, and modular data architecture patterns and standards.
• Develop architecture blueprints and reference architecture for enterprise data platforms.

Data Strategy & Governance

• Define enterprise data strategy, including data sourcing, storage, access, and lifecycle management.
• Design and implement data governance frameworks (data quality, lineage, cataloging, master data management).
• Establish data models, standards, and metadata management practices.

Data Integration & Interoperability

• Design data ingestion frameworks (batch and real-time/streaming) from diverse structured and unstructured sources.
• Enable interoperability across enterprise systems through standardized data exchange and integration patterns.
• Define data contracts and APIs for data access and sharing.

Cloud & Data Platform Modernization

• Drive cloud-based data lake and lakehouse implementations (AWS/Azure/GCP).
• Modernize legacy data platforms to scalable, distributed data architectures.
• Optimize data performance, storage, and cost efficiency.

Analytics & Data Consumption Enablement

• Enable downstream BI, reporting, and advanced analytics/AI-ML use cases.
• Design data marts, semantic layers, and curated datasets for business consumption.

Regulatory Compliance & Data Security

• Ensure data platforms comply with GxP, 21 CFR Part 11, and other Life Sciences regulatory standards.
• Implement data security, privacy, and access control mechanisms.

Stakeholder Management and Collaboration

• Work with vendors and platform providers for implementation and optimization.
• Support client discussions, workshops, and solution presentations.

Best Practices & Capability Building

• Establish data engineering and architecture best practices, reusable assets, and accelerators.
• Contribute to knowledge building, documentation, and capability development within teams.

Skills Required:

• 5+ years of experience in data architecture / data engineering.
• Strong experience in Data Lake architecture and implementation.
• Strong domain experience in Life Sciences / Pharma / Biotech.
• Expertise in data modeling, ETL/ELT pipelines, and big data processing frameworks.
• Hands-on experience with cloud data platforms (AWS S3, Azure Data Lake).
• Knowledge of data governance, cataloging, and lineage tools.
• Familiarity with structured and unstructured data processing.
• Understanding of GxP and regulatory data compliance requirements.

Preferred Qualifications

• Experience with tools/platforms like Databricks, Snowflake, and Azure Data Factory.
• Exposure to AI/ML and advanced analytics use cases on data lakes.
• Experience with data lakehouse architecture.
• TOGAF / Cloud certifications (AWS/Azure).

Education : Relevant educational qualification in Computer Science, Information Technology, Engineering, Data Science, or a related field

Key Skills

Education

Any Graduate