Skills Needed: Building manual and automated pipelines in Snowflake, hands on experience with data models, performance optimization, access controls, data warehouse and data lakes, etc. Experience with transactional data systems such as ERPs or Trading platforms. Knowledge of AWS and its services such as S3, Glue, Lambda, EMR, Redshift, etc.
Title: Sr Snowflake Engineer Location: Houston, TX or Allenstown, PA | Hybrid (3-Days Onsite) Contract
This role will help build out the data platform foundation and then deliver business use cases through an iterative, Agile wave-based approach. Each delivery wave will extend reusable platform capabilities while moving prioritized Trading Risk data use cases from design through production. Success will be demonstrated through a production-ready data platform with standardized architecture, security, governance, data quality, CI/CD, and operational practices that can efficiently onboard additional use cases and teams.
The Snowflake Data Engineer will:
- Build automated and manual data ingestion pipelines into Snowflake.
- Implement transformations and mappings across the ingestion layers.
- Develop reusable data transformation components and UDFs.
- Support architectural decisions related to connectivity, platform configuration, and workload design.
- Optimize pipelines and Snowflake workloads for performance, reliability, and cost.
- Deliver prioritized use cases through iterative Design, Build, Business Testing, Operational Testing, and Production cycles.
- Support UAT, production readiness, deployment, rollback, recovery, and business continuity procedures.
- Help establish reusable platform capabilities so that subsequent use cases can be delivered more efficiently.
Must Have:
- Hands-on experience with Snowflake, including data modeling, performance optimization, access controls, and scalable warehouse/lakehouse patterns.
- Functional understanding of transactional data systems, such as a Trading or ERP system.
- Working knowledge of AWS services such as S3, Glue, Lambda, Athena, EMR, Redshift, and cloud-based data architectures.