Solutions Architect

About SunnyData: 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, you will be instrumental in driving customer success, both in terms of technical execution and pre-sales support.

• 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

• Experience: 7+ years as a hands-on Solutions Architect and/or Sr. Data Engineer designing and more recent experience implementing data solutions with a focus on DataBricks.
• Technical Proficiency: Expertise in Data Engineering technologies (e.g., Spark, Hadoop, Kafka), Databricks platform, and data science/machine learning technologies (e.g., pandas, scikit-learn).
• 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.
• Programming Skills: Proficiency in Java, Python, and/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.

Education

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

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