Solutions Architect

At SunnyData, a leading Databricks technology partner, our mission is to empower customers with scalable architectures, robust data engineering pipelines, seamless data consumption layers, and advanced ML and AI applications. As a Solutions Architect specialized in Platform, Cloud Infrastructure and Security, you will design and implement scalable data infrastructure while meeting security and compliance requirements. The Impact You Will Have Customer Engagement:

Serve as a trusted advisor to customers, guiding them through their data engineering and data architecture needs with a focus on Databricks solutions. In this role you will split your time evenly between billable and pre-sales activity.

Technical Leadership:

Design, build, and deploy comprehensive data solutions that capture, transform, and leverage data to support AI, ML, and business intelligence initiatives.

Pre-Sales Support:

Collaborate with sales teams to present technical solutions to prospective clients, demonstrating the value of SunnyData's offerings.

Project Oversight:

Manage multiple customer accounts, ensuring timely delivery of solutions and tracking progress to report outcomes.

Solution Design:

Architect data solutions, incorporating best practices in data governance, security, and quality.

Data Analysis:

Evaluate data sources for their value, recommending data inclusion strategies to enhance analytical processes.

Cross-Functional Collaboration and Leadership :

Lead internal teams, provide direction and mentorship to project teams to deliver solutions and educate end users on data products and analytic environments.

Problem Resolution:

Perform system analysis, assess and resolve data and system defects, and apply appropriate corrections.

Quality Assurance:

Test data movement, transformation code, and data components to ensure accuracy and reliability.

Required Experience 7+ years as a hands-on Solutions Architect with experience in Data Security or related areas. Expertise in two or more of the following: Cloud Security, Cloud Networking, Encryption, Governance, Privacy, Trust, Safety, Authentication, Identity Management, Access Control, Key Management, Inter-Service Authentication, Secure Application Frameworks, Detection & Response

Infrastructure and Security:

designing data platforms on cloud infrastructure and services (AWS, Azure, or GCP), using best practices in cloud security and networking.

Technical Proficiency:

Expertise in Data Engineering technologies (e.g., Spark, Hadoop, Kafka), Databricks platform, cloud platforms, security, automation, networking, or identity management

Architecture and leadership skills:

In-depth understanding of the end to end data analytics workflow (e.g., data modeling, ETL processes, and data integration) using modern data engineering techniques; Ability to lead complex architecture requirements(discovery), solution design sessions and build out implementation architecture blueprints that can be implemented by data-engineering and analytics teams.

DevOps/DevSecOps:

SDLC tooling such as Github, Gitlab, Continuous Inspection such as SonarQube and Artifact Management such as Artifactory

IaC tools

like Terraform

Programming Skills:

Proficiency in Python, Java, or Scala

Cloud Platforms:

AWS, Azure, and/or GCP.

SQL Expertise:

Ability to write, debug, and optimize SQL queries.

Client-Facing Skills:

Strong written and verbal communication skills with experience in client-facing roles.

Presentation Skills:

Ability to create and deliver detailed presentations to clients and stakeholders.

Documentation:

Experience in creating detailed solution documentation including POCs, roadmaps, sequence diagrams, class hierarchies, and logical system views.

Team Leadership:

Experience leading teams and mentoring other engineers.

End-to-End Solutions:

Ability to develop end-to-end technical solutions into production, ensuring performance, security, scalability, and robust data integration.

Preferred Experience Distributed Storage:

Familiarity with cloud and distributed data storage systems such as S3, ADLS, HDFS, GCS, Kudu, ElasticSearch/Solr, Cassandra, or other NoSQL storage systems.

Data Integration:

Experience with data integration technologies like Spark, Kafka, Streamsets, Matillion, Fivetran, NiFi, AWS Data Migration Services, Azure DataFactory, and Informatica Intelligent Cloud Services (IICS).

Software Development Lifecycle:

Comprehensive experience with the complete software development lifecycle including design, documentation, implementation, testing, and deployment.

Automated Pipelines:

Expertise in automated data transformation and curation using tools like dbt, Spark, Spark streaming, and automat