Data and Analytics Principal Solution Architect

5 days ago

delhi, India INFINIMINDS Full time

Key Responsibilities:

1. Pre-Sales Responsibilities:

- Engage with potential clients to understand their data needs, challenges, and objectives.
- Lead discovery sessions, gather requirements, and develop high-level solution architectures for data and analytics initiatives.
- Prepare and present compelling proposals, solution demos, and proof of concepts that demonstrate the value of data and analytics services.
- Respond to RFPs/RFIs by developing solution architecture, resource estimates, and project timelines.
- Collaborate with the sales and technical teams to define the scope, costs, and risks associated with proposed solutions.
- Serve as a subject matter expert (SME) for data management, analytics, and AI/ML technologies, providing insights and technical leadership during client engagements.

2. Delivery Responsibilities:

- Lead the design and implementation of data platforms and advanced analytics solutions, ensuring alignment with client goals and industry best practices.
- Architect scalable and secure data solutions using cloud technologies (preferably Azure), SQL, data lakes, data warehouses, and analytics platforms.
- Oversee the development of data pipelines, ETL processes, data integration solutions, and analytics dashboards using Power BI or similar tools.
- Ensure the successful delivery of AI/ML projects, including model development, deployment, and operationalization.
- Implement data governance frameworks to ensure data quality, security, and compliance across platforms.
- Manage project teams, mentor technical staff, and provide hands-on technical guidance where needed.
- Interface with key stakeholders to manage project timelines, risks, and escalations while maintaining strong communication with clients.
- Conduct regular solution reviews and health checks to optimize existing data architectures and identify opportunities for improvement.

3. Solution Development & Thought Leadership:

- Stay updated with the latest trends in data, analytics, and AI technologies to ensure client solutions remain innovative and competitive.
- Develop and contribute to reusable frameworks, best practices, and templates that improve delivery efficiency and consistency.
- Participate in industry forums, conferences, and webinars to represent our Client and promote its data expertise.
- Lead internal knowledge-sharing sessions and contribute to the development of white papers, case studies, and blog posts.

Qualifications:

- Minimum 15 years of experience in data architecture, analytics, and business intelligence, with a focus on enterprise-scale solutions.
- Strong expertise in architecting and delivering cloud-based data platforms (preferably Azure) including Azure Data Factory, Azure Synapse Analytics, Azure SQL Database, and Power BI.
- Experience with AI/ML technologies, such as Azure Machine Learning, Python, R, or other data science platforms.
- In-depth knowledge of data modeling, data warehousing, and ETL/ELT pipelines.
- Expertise in data governance, security, and compliance best practices (GDPR, HIPAA, etc.).
- Strong understanding of big data architectures and tools such as Spark, Hadoop, Databricks, and data lakes.
- Excellent client-facing communication and presentation skills, with a proven ability to translate complex technical concepts into business value.
- Experience leading pre-sales activities, including client discovery, solution architecture, and RFP responses.
- Strong project management and leadership skills with experience managing cross-functional teams.
- Relevant certifications such as Microsoft Certified: Azure Solutions Architect Expert, Microsoft Certified: Data Engineer Associate, or similar are highly preferred.

Preferred Skills:

- Experience with additional cloud platforms (AWS, Google Cloud) is a plus.
- Knowledge of data visualization and reporting tools beyond Power BI (e.g., Tableau, Qlik).
- Experience with data migration tools like Talend, Informatica, or Azure Data Factory.
- Familiarity with DevOps practices and tools for data pipeline automation and deployment.

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