Solutions Architect - Platform, Cloud Infrastructure and Security

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, 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, 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 automated pipelines.

• Workflow Management: Experience with workflow management and orchestration tools like Airflow, AWS Managed Airflow, Luigi, NiFi .

• Certifications (at least 2 of the following): Associate Developer for Apache Spark; Data Engineer Associate; Professional Data Engineer; Machine Learning Associate; Professional ML Engineer

Education: Bachelor’s Degree in Computer Science or Engineering related field.