Presales Solution Architect - Data Management
Role details
Job location
Tech stack
Job description
Domain experience in verticals/industries, e.g. financial services, healthcare etcDrive revenue and progress the transaction as a technical advisor, technical consultant, solution architect to the transaction and to the customerConduct solution sessions/workshops and demos with strategic accountsConsulting with prospective customers on their data challenges, objectives and required capabilities to meet the needDrafting, designing & developing technical reference architectures for prospective customers and use cases: data migration, cataloging, and data qualityLeading technical design sessions, providing detailed product architecture overviews and determining the customer's design, integration, and developmentConsult with strategic account teamConsult with Product Management and software leadership about current industry trends and technology needs
Requirements
10+ Years Solution architecture expertise in DataOps (data management, analytics, data governance) use cases, technologies, and solutionsExperience working with modern data management practices (e.g. ETL, governance, stewardship, master data management, data catalog, data lineage, data warehousing, data modelling, data lake, data vault, data mesh, analytics, dashboarding & reporting)Experience working with ETL/data integration/data catalog/data governance and data quality frameworks (e.g., Pentaho, Talend, Informatica, SSIS/ADF, Apache Airflow)Knowledge of BI and reporting applications (OBIEE, Business Objects, Cognos, Tableau, Qlik, MicroStrategy, Looker, Domo, PowerBI)Structured data/SQL engines (e.g., Postgres, MySQL, Oracle, SQL Server, Snowflake, Redshift)Knowledge of unstructured data indexing and NoSQL data engines (e.g. MongoDB, ArangoDB, SOLR, Cassandra, Spark)Container deployment and management (e.g., Docker, OpenShift, Kubernetes)Technical sales and revenue process experienceExperience w/ design thinking and solutioningIdentify emerging DataOps and AI challenges and solutions required to overccomeGuide in the application of enterprise data and metadata management technologies