Snowflake Architect

4 weeks ago

Anywhere in IndiaMultiple LocationsBangalore Mobile Programming Full time

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
Roles & Responsibilities:
  • 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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