Snowflake Architect
$115.5k - $135.5kCognizant Technology Solutions
About AI & Analytics: Artificial intelligence (AI) and the data it collects and analyzes will soon sit at the core of all intelligent, human-centric businesses. By decoding customer needs, preferences, and behaviors, our clients can understand exactly what services, products, and experiences their consumers need. Within AI & Analytics, we work to design the future—a future in which trial-and-error business decisions have been replaced by informed choices and data-supported strategies. By applying AI and data science, we help leading companies to prototype, refine, validate, and scale their AI and analytics products and delivery models. Cognizant’s AIA practice takes insights that are buried in data and provides businesses a clear way to transform how they source, interpret and consume their information. Our clients need flexible data structures and a streamlined data architecture that quickly turns data resources into informative, meaningful intelligence. Please note, this role is not able to offer visa transfer or sponsorship now or in the future Job Summary We are seeking a skilled Data Engineer with strong expertise in Snowflake, AWS, DBT, Python, PySpark, and Unix to support data engineering initiatives within the Life Sciences domain. The ideal candidate will have hands-on experience in building scalable data pipelines, designing ETL processes, and managing end-to-end data workflows in cloud environments. This role will focus on enabling data integration, transformation, and analytics solutions that support business and operational objectives. In this role, you will: Design, develop, and maintain scalable data pipelines using Snowflake, AWS, Python, and PySpark. Build and optimize ETL/ELT processes for ingesting, transforming, and loading data from multiple source systems. Develop data transformation models using DBT and implement best practices for data quality and governance. Work with AWS services such as S3, Glue, Lambda, EMR, Redshift, and other cloud-native data services. Design and support data warehousing solutions in Snowflake, including schema design, performance tuning, and optimization. Monitor and troubleshoot data workflows, ensuring reliability, accuracy, and timely delivery. Collaborate with business stakeholders, data analysts, and application teams to understand data requirements and implement solutions. Participate in data migration and modernization initiatives within Life Sciences environments. Develop and maintain technical documentation, data flow diagrams, and operational procedures. Ensure compliance with security, regulatory, and data governance standards applicable to Life Sciences organizations. What you need to have to be considered Strong hands-on experience with Snowflake Data Warehouse. Expertise in AWS Cloud Services and cloud-based data architecture. Experience with DBT (Data Build Tool) for data transformation and modeling. Proficiency in Python and PySpark for data processing and automation. Strong understanding of ETL/ELT frameworks, data integration, and pipeline orchestration. Experience with Unix/Linux scripting and environment management. Knowledge of SQL optimization, data modeling, and database design principles. Familiarity with CI/CD practices and version control tools such as Git.
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