We are Sisense.
Sisense enables pulling and extracting data from endless data sources (RDBMS, Cloud Storages, Files and Web Services). As a Data Engineer in the Solution Architects team, you will be championing our Best Practices and guiding our strategic customers to ensure optimal performance and successful implementations.
What we're looking for?
The role will include a focus on the data ingestion aspect as part of customers design, implementation and support for Sisense BI project solutions, as well as develop automation workflows with the intent of optimizing the customer’s adoption and future growth.
What You'll Do?
- Technical lead for the different data sources being utilized by Sisense most advance customers from technical requirements and data pipeline design to training and solution delivery
- Execute an extensive analysis to design the solution and describe it through architecture and design artifacts
- Present proposed solutions and maintain ongoing technical discussions with the customer across all levels, including exec
- Coach and supervise technical team members
- Build Data-prep best practices for technical deployments and extensive performance tests to our customers
- Work with R&D Data Connectors teams to design sophisticated and non- trivial solutions
- Work with the Data Product Managers to understand product road-map and play an active role in defining features and changes in product requirements that influence the future customers usage of the product
- Lead complex troubleshooting forums including communication with customers technical exec -a status update, expectation setting, present solution
What should You have?
4+ years of experience with:
- Data warehouse space
- Custom ETL design, implementation, and maintenance
- Working with either a Map Reduce or an MPP system
- Schema design and dimensional data modeling
- Database Performance tuning
- Experience as a Data engineer or Java/Scala developer
- Development experience in a Big Data Cloud environment (at least one of AWS, Azure, GCP)
- Experience with big data solutions like Redshift, BigQuery, Snowflake, Hive, Presto, Neo4J, etc