AWS - Senior Data Engineer - LS - CWR

DATA & AI • CLOUD DATA ENGINEERING • LIFE SCIENCES

Manager / Senior Manager

Role purpose

Lead the engineering and implementation of enterprise-scale cloud data platforms using AWS and Snowflake. The role combines deep hands-on data engineering with technical leadership: translating architecture and business requirements into production-grade solutions that are scalable, secure, performant, supportable and cost-efficient.

Life Sciences / Pharma / Biotech / MedTech experience is preferred, particularly in regulated, high-volume or analytics-intensive data environments.

What you will own

• Design, build and optimize enterprise-scale data pipelines across AWS and Snowflake, spanning ingestion, transformation, consumption and operational monitoring.

• Engineer batch, near-real-time and streaming patterns for structured, semi-structured and unstructured data; translate solution architecture into detailed engineering designs and deployable components.

• Build reusable frameworks, utilities and engineering patterns rather than one-off pipelines; design for scalability, resilience, maintainability and operational support.

• Own technical delivery across design, build, testing, migration, deployment and production stabilization.

Core engineering accountabilities

Snowflake engineering — Build and optimize databases, schemas, tables, views, warehouses and data-sharing patterns; use Snowpipe, Streams & Tasks, Dynamic Tables and Snowpark where appropriate; optimize SQL, compute, concurrency, storage and cost; implement RBAC, masking and access policies.

AWS data engineering — Engineer solutions using relevant services such as S3, Glue, Lambda, Step Functions, DMS, Kinesis, IAM and CloudWatch; integrate cloud, SaaS, API, enterprise and on-premise sources; implement monitoring, logging, recovery and operational resilience.

ETL / ELT & integration — Design complex ETL/ELT pipelines, source-to-target mappings, transformation logic, reconciliation controls and orchestration; establish restartability, exception handling, auditability and standardized integration patterns.

Data modelling & quality — Implement logical and physical models for operational, reporting, analytics and AI use cases; automate completeness, validity, consistency, uniqueness and reconciliation controls; enable metadata, lineage and observability.

DevOps & engineering standards — Establish CI/CD, Infrastructure as Code, automated testing, version control, code review and release-management practices; improve engineering productivity through reusable components and automation.

Life Sciences experience – preferred

Experience with data platforms supporting one or more of the following domains is a strong advantage:

• Clinical Development / Clinical Operations

• Clinical Data Management & Biostatistics

• Pharmacovigilance / Drug Safety

• Regulatory Affairs

• Research & Discovery

• Medical Affairs

• Manufacturing & Quality

• Commercial / Patient Data

• Real-World Data / Real-World Evidence

The individual should understand that Life Sciences data engineering is not only about moving data. Data quality, traceability, lineage, controlled access and reproducibility can be as important as engineering performance. Experience in environments subject to GxP, 21 CFR Part 11, GDPR or other applicable privacy/regulatory requirements is advantageous.

Core technical expectations

Engineering leadership expectations

• Lead distributed data-engineering teams and convert complex requirements into executable engineering work packages.

• Review solution designs, data models, pipeline patterns and critical code; retain enough hands-on depth to challenge designs and diagnose complex issues.

• Establish and enforce engineering standards; own performance, production and technical escalations.

• Manage technical dependencies, engineering estimates and delivery risks; mentor senior engineers and technical leads.

• Work effectively with Data Architects, Cloud Architects, Security, DevOps, Analytics and business teams, and explain engineering trade-offs clearly to senior client stakeholders.

Level expectations

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.