Solution Architect - Data & AI

at  Epergne Solutions

Epergne Solutions is looking for Solutions Architect - Data & AI

DESCRIPTION

We are seeking a highly skilled and experienced Solution Architect – Data Analytics for AWS to join our team and play a crucial role in designing and implementing data analytics solutions on the Amazon Web Services (AWS) platform. The ideal candidate will have a deep understanding of data analytics technologies, AWS services, and a track record of architecting scalable and efficient data solutions to address complex business challenges.

REQUIREMENTS

Proven experience (3+ years) as a Solution Architect or similar role, with a focus on data analytics and AWS cloud technologies.
Extensive hands-on experience with AWS services such as Amazon Redshift, Amazon EMR, Amazon Kinesis, AWS Glue, etc.
Strong understanding of data modeling, ETL processes, data warehousing, and data governance principles.
Familiarity with programming/scripting languages like Python, SQL, and experience with data manipulation and transformation.
Excellent problem-solving skills with the ability to design and implement creative solutions for complex data challenges.
Strong communication skills to effectively collaborate with technical and non-technical teams. AWS certifications (e.g., AWS Certified Solutions Architect - Associate/Professional, AWS Certified Data Analytics - Specialty)
Experience in working with Agile methodologies and version control systems.
Proven ability to work independently and manage multiple priorities effectively.

EDUCATION AND CERTIFICATION

Bachelors or associate degree in IT discipline
Certification in AWS Certified: Solution Architect and AWS Certified: Data Analytics Specialty are must.
EA: 19S9678

Solution Design: Collaborate with cross-functional teams to gather requirements, design end-to- end data analytics solutions, and create architectural diagrams and documentation.
AWS Expertise: Leverage your extensive knowledge of AWS services such as Amazon Redshift, Amazon EMR, Amazon Kinesis, AWS Glue, and others to architect robust data analytics platforms. Data Modeling: Design and optimize data models to support reporting, analytics, and business intelligence needs while ensuring data integrity and performance.
Scalability and Performance: Architect solutions that are scalable, high-performing, and cost- efficient, considering factors like data volume, query complexity, and user concurrency.
Data Integration: Define data integration strategies to extract, transform, and load (ETL) data from various sources into the analytics platform, ensuring data consistency and quality.
Security and Compliance: Implement security measures and best practices to protect sensitive data and ensure compliance with industry standards and regulations.
Troubleshooting and Optimization: Identify and resolve performance bottlenecks, data quality issues, and other challenges that may arise during implementation and operation. Collaboration: Work closely with data engineers, data scientists, business analysts, and stakeholders to understand their needs and provide technical guidance.
Innovation: Stay up-to-date with industry trends and emerging technologies to recommend innovative solutions that enhance our data analytics capabilities.