Job Description

Role - Solution Architect (Data Engineering)

Location Remote, USA (The candidate will be required to travel to the client's location in Boston, MA, as required)

Duration 6 months + possible extension

Experience Required 12 years minimum 16 years maximum (Dynamic personality)

Number of Internal interviews with Nagarro 1 or 2

Client Interview (Y/N) Yes, 1 or 2

Mode of client interview Virtual (Video)

Tentative start date ASAP

DM Notes: Solution Architect with strong experience in end go end enterprise level data related initiatives and have also experience/knowledge in working GenAI based solutions.

Must have Skills: AWS Code Deploy, Technical Solutioning (Expert), Cloud architecture (Strong),

Good To Have Skills: Fast API (Strong), Machine Learning Solution Design (Capable).

Job Description :

• Develop the Platform & Solutions Architecture at Scale Define and execute a comprehensive AWS-based data roadmap, embracing modern data Architecture patterns (serverless computing, containerization, event-driven architectures) for unstructured, semi-structured and structured datasets.
• Deep understanding of modern data architectures, metadata management, storage solutions, and AI-driven data processing techniques to support enterprise-wide search, retrieval, compliance, and analytics Develop data ingestion, transformation, and enrichment pipelines for documents, messages, images, videos, and other unstructured content across platforms. Define storage strategies including structured and semi-structured repositories, object stores, and data lakes for optimal data retrieval and compliance.
• Drive continuous improvement of data governance, security, and compliance, aligning with international regulations (GDPR, CCPA, etc.).
• Drive Data Platform Engineering Oversee the end-to-end engineering of data platforms, supporting data ingestion pipelines for batch, file-based, and API-based integrations.
• Build and optimize data storage solutions, including data lakes, data warehouses, vector databases, graph databases, and index stores. Architect the scalable, reliable, scalable Design and compute layers with reusable ETL components, AI/ML pipelines for metadata preprocessing, chunking, and tokenization.
• Implement robust observability and monitoring frameworks to ensure data quality, minimize downtime, and optimize performance across the enterprise.
• Collaborate with cross-functional teams including Data Engineering, Cloud Infrastructure, AI/ML teams, and Data Analysts to align solutions with business needs.
• Translate business requirements into scalable data solutions.
• Understand business requirements by collaborating with business, data, and technical teams.
• Align priorities, architecture decisions, and implementations to maximize business value.
• Partner with stakeholders to ensure data solutions support business goals while meeting compliance and security standards.

YOU-'RE GOOD AT AWS-Centric Architecture:

• Strong expertise in designing, deploying, and managing cloud-native data solutions (e.g., S3, Redshift, EMR, Lambda, Kinesis, DynamoDB).

Large-Scale Data Architecture:

• Experience with high-volume data pipelines, including real-time streaming, event-driven microservices, and petabyte-scale data processing.

Advanced Analytics & AI:

• Familiarity with machine learning pipelines, generative AI, embeddings, and vector databases, along with a record of accomplishment of productionizing ML models.
• Strong expertise in unstructured data architectures, including document repositories, object storage, and NoSQL databases.
• Experience designing and implementing data lakes, data warehouses, or knowledge graphs for unstructured data.
• Hands-on experience with data integration frameworks (ETL, ELT, API-based ingestion, event-driven architectures).
• Familiarity with cloud-native solutions (Azure, AWS, GCP) for data storage, processing, and security.
• Experience with data governance and compliance frameworks for managing sensitive corporate information.
• Knowledge of GenAI technologies and selling their value, Including (conceptually), LLM access and training, and data handling in the GenAI context.

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