AWS - Senior Data Engineer - LS - CWR

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
Senior Manager: 14–16 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
12–14 YEARS
SENIOR MANAGER
14–16 YEARS
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.

Work arrangement
Hybrid