Job Summary
Lead role responsible for designing robust data solutions that integrate Amazon S3 Python and Snowflake SQL in a hybrid work model with day shift schedules and no travel requirement focusing on scalable data pipelines secure storage and efficient analytics that enable the organization to make timely and ethical data driven decisions for global business impact.
Strong experience in snowflake python data engineering ML and other related skills.
Responsibilities
- Design comprehensive data architectures that integrate Amazon S3 Python and Snowflake SQL to create secure scalable and high performance data platforms that support complex analytical and operational workloads across the organization.
- Develop optimized data ingestion and transformation workflows using Python to move structured and semi structured data into Amazon S3 and Snowflake environments ensuring reliable and consistent data availability for downstream consumption.
- Implement efficient Snowflake SQL models including schemas views and query patterns that enable fast reporting and analytics while maintaining strong data governance and minimizing resource consumption.
- Coordinate with product and business stakeholders to translate analytical and reporting requirements into clear data architecture designs ensuring that the implemented solutions deliver measurable value and align to enterprise strategies.
- Establish robust data quality validation routines using Python scripts and Snowflake SQL checks that detect anomalies enforce data standards and enhance trust in data used for decision making across multiple teams.
- Configure secure access patterns to Amazon S3 objects and Snowflake datasets by defining roles policies and integration flows that protect sensitive information while supporting compliant data sharing and collaboration.
- Optimize data storage strategies in Amazon S3 by organizing buckets objects and lifecycle rules that reduce costs improve retrieval performance and ensure long term durability for critical datasets.
- Create reusable Python components for data processing logging and error handling that standardize engineering practices promote automation and reduce maintenance overhead for recurring data workflows.
- Monitor and fine tune Snowflake computation and storage usage through query analysis workload management and resource configuration adjustments that maintain predictable performance within budget constraints.
- Collaborate with hybrid teams across locations through clear documentation architectural diagrams and knowledge sharing sessions that enable consistent understanding of data solutions and support continuity of operations.
- Guide implementation activities by reviewing code design artifacts and test results to ensure that delivered data solutions strictly follow defined architectures meet non functional requirements and can be reliably operated by engineering teams.
- Evaluate new cloud data services features and patterns related to Amazon S3 Python ecosystems and Snowflake capabilities to propose incremental improvements that strengthen the company data posture and support responsible innovation.
- Drive adherence to security privacy and compliance standards in all data architecture decisions so that organizational data practices support societal trust regulatory alignment and ethical use of information.
Qualifications
- Demonstrate extensive experience architecting cloud based data solutions that rely on Amazon S3 for storage Python for processing and Snowflake SQL for analytics gained over several years of hands on project delivery.
- Show strong proficiency in advanced Snowflake SQL capabilities including complex joins window functions performance tuning and data sharing features that are essential for enterprise scale analytical platforms.
- Apply expert level Python skills to build resilient data pipelines orchestration logic and integration scripts using widely adopted libraries and practices suitable for production ready data engineering.
- Exhibit solid understanding of cloud data security principles such as encryption access control and segregation of data environments ensuring that architectures protect sensitive information and comply with standards.
- Utilize proven experience in designing data models and integration patterns that handle large volumes of structured and semi structured data with emphasis on reliability auditability and maintainability.
- Demonstrate strong communication and collaboration abilities to work effectively in hybrid teams conveying complex architectural concepts in clear language to technical and nontechnical partners.
- Leverage prior exposure to modern data engineering tooling for version control automation and testing to ensure that implemented data solutions remain stable traceable and straightforward to enhance over time.
Certifications Required
good to have
About Cognizant:
Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, bridging the gap between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization’s unique context into technology systems that amplify human potential, drive tangible outcomes and keep global enterprises ahead in a fast-changing world. See how at cognizant.ai or @cognizant.
Additional employment information
Compensation information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview.
Cognizant is an equal opportunity employer. Your application and candidacy will not be considered based on race, color, sex, religion, creed, sexual orientation, gender identity, national origin, disability, genetic information, pregnancy, veteran status or any other characteristic protected by federal, state or local laws.
