DATA & AI • CLOUD DATA ENGINEERING • LIFE SCIENCES
AWS Snowflake – Senior Data Engineer - Contractual role
Manager / Senior Manager
LEVEL | EXPERIENCE | DOMAIN | CORE PLATFORM |
Manager / Senior Manager | Manager: 12–14 years | Life Sciences preferred | AWS | Snowflake | Data Engineering |
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
MUST HAVE | GOOD TO HAVE |
• Strong hands-on Snowflake and AWS experience • Enterprise-scale cloud data platforms and pipelines • Advanced SQL and strong data-engineering fundamentals • ETL/ELT architecture and development • Snowflake performance and workload management • Data modelling and database design • Production-grade logging, monitoring, reconciliation and error handling • Security, governance, lineage and data-quality fundamentals • Design-to-production implementation ownership | • Python • Snowpark, Snowpipe, Streams & Tasks, Dynamic Tables • dbt and/or Airflow or equivalent orchestration • Kafka / Kinesis or other streaming technologies • Terraform / CloudFormation • Git-based CI/CD • Collibra / Alation or equivalent • Databricks or other modern data platforms • AWS and/or Snowflake certifications |
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
MANAGER | SENIOR MANAGER |
• Operate as a senior engineering lead with strong hands-on technical credibility. • Independently lead a significant data-engineering workstream and team. • Own complex pipeline and integration design, engineering estimation and delivery planning. • Lead code/design reviews, performance troubleshooting and engineering-quality governance. • Own technical risks, dependencies and client-facing engineering discussions. | • Operate as engineering leader for a complex enterprise data platform or transformation program. • Own engineering strategy across multiple workstreams and larger distributed teams. • Govern engineering standards, reusable frameworks and cross-team technical consistency. • Own complex technical escalations and challenge architecture/engineering decisions constructively. • Lead senior client discussions, multi-release planning, modernization and engineering-productivity improvement; coach Managers and technical leads. |
コグニザントについて
コグニザント（NASDAQ: CTSH）は、AI Builderおよびテクノロジーサービスプロバイダーとして、お客様にフルスタックのAIソリューションを構築することで、AI投資と企業価値を結ぶ架け橋となっています。業界、ビジネスプロセス、エンジニアリングに関する当社の深い専門知識を活かし、組織固有のビジネス環境をテクノロジー・システムに組み込みます。これにより、人間の可能性を最大限に引き出し、確かな成果を実現するとともに、急速に変化する世界においてグローバル企業が常に一歩先を行くための支援を行っています。 詳細については、cognizant.ai をご覧ください。
雇用に関する追加情報
本募集に記載されている報酬情報は、掲載日時点で正確なものです。Cognizantは、適用される法令に従い、いつでも本情報を変更する権利を留保します。
応募者は、対面またはビデオ会議による面接への参加を求められる場合があります。また、各面接の際に、現在有効な州政府または政府発行の身分証明書の提示を求められる場合があります。
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