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Snowflake Architect
3 weeks ago
Skills Required:
- 8+ years of experience architecting, designing, developing and optimizing large scale data solutions, out of which at least 5 years is in SaaS domain
- Strong experience in building with GCP and/or AWS infrastructure and services
- Expert knowledge and deep hands-on experience with cloud data platforms/services such as Snowflake, Azure Synapse, Informatica IICS, Databricks etc. Hands on experience with Snowflake is a must-have
- Excellent design and development experience with SQL and NoSQL databases, OLTP and OLAP databases
- Proficient in dimensional modeling concepts and techniques
- Experience in design and development of custom ETL pipelines using SQL, scripting languages (Python/ Shell) and well defined API's
- Excellent verbal and written communication skills
- Experience with data security and privacy best practices and threat modeling
- Fluency developing tools, applications and backend services in Python
- Strong experience with Snowflake and PostgreSQL including writing complex queries and performance tuning
- Good understanding of the challenges of data quality and data testing and how to mitigate them
- Command of Python and experience with modern data stack tools such as Airflow, dbt, Airbyte, Fivetran, Stitch, Segment, Apache Superset, Snowflake, Spark etc.
- Education in Computer Science, Engineering or training in a related field
- Knowledge of Pub/Sub architectures or other messaging / streaming technologies
- Experience with AI/ML or GenAI infrastructure or applications
- Establish the best practices to secure the data and infrastructure, and evangelize the security design and development
- Build data pipelines and structures that power predictive models and intelligence services on large-scale datasets with high uptime and production level quality
- Integrate with CRMs to ingest customer data to build analytics data sets
- Interface with internal Data Science, Engineering and Data Consumer teams to understand customer and product data needs
- Own data quality throughout all stages of acquisition and processing, including data collection, ETL/data wrangling, data normalization and ground truth generation
- Advocate for better software development tools, practices to help build a transparent engineering culture
(ref:hirist.tech)
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