Job Description & How to Apply Below
Job Title :
Technical Architect – Data Platform & Analytics (Life Sciences)
Reports To:
Senior Director, Data Management & Analytics
Employment Type:
Full-time
Workplace Type: Remote
Timing: Upto 11 pm IST (Overlap with PST)
The Celito Team
The Celito Team architects the buildout of simplified, integrated, and compliant technology stacks. With both consulting and products, our expertise can help our customers save time and money as they move from strategic Clinical & Quality management all the way to widespread and profitable commercialisation.
Job Overview
The Senior Data & Analytics Engineer / Technical Lead will serve as a key technical contributor for designing, implementing, and supporting scalable enterprise data and analytics solutions across AWS and/or Azure ecosystems. This role is responsible for leading hands-on engineering activities across modern cloud-native data platforms, including data ingestion, transformation, orchestration, reporting, and production support functions using technologies such as Databricks, Snowflake, SQL, Python, and PySpark.
The individual will collaborate closely with architects, business stakeholders, analysts, and engineering teams to deliver scalable, maintainable, and analytics-ready solutions while supporting Dev Ops, CI/CD, operational excellence, and AI-enabled data initiatives across enterprise environments.
Responsibilities and Duties
Design and implement scalable enterprise data and analytics solutions across AWS and/or Azure ecosystems
Act as a hands-on technical lead contributing directly to architecture, engineering, optimisation, troubleshooting, deployments, and production support activities
Build and maintain ingestion, transformation, orchestration, integration, API, and reporting pipelines using modern cloud-native technologies, including Snowflake and Databricks
Design scalable enterprise data models, semantic/business layers, and analytics-ready data structures for reporting and analytics platforms
Support analytics and reporting platforms, including Tableau, Power BI, and Spotfire
Support Dev Ops, CI/CD, deployment automation, release management, operational support, and environment management processes
Manage production support activities, including monitoring, incident management, root cause analysis, performance optimisation, and issue resolution
Support implementation of AI/ML and GenAI-enabled analytics solutions, reusable frameworks, and AI-ready data platforms
Collaborate with architects, business stakeholders, analysts, and engineering teams to deliver scalable and maintainable solutions
Contribute to technical standards, engineering best practices, documentation, governance, and operational excellence initiatives
Qualifications
Bachelor’s degree in computer science/software engineering or equivalent combination of education and experience
8+ years of total IT experience
6+ years of hands-on experience designing and implementing Modern Data Platforms and Analytics solutions
Experience working in Life Sciences, Pharma or Med Tech domains
Strong hands-on expertise in SQL, Python, PySpark, and cloud-native data engineering technologies
Strong experience with Databricks, Snowflake, Redshift, Synapse, or similar modern data platforms
Experience with AWS and/or Azure cloud services
Strong ETL/ELT, orchestration, pipeline development, API integrations, and performance optimisation experience
Experience with Airflow, Step Functions, ADF, or similar orchestration technologies
Strong understanding of enterprise data modelling, dimensional modelling, semantic/business layer modelling, and modern data warehousing concepts
Experience supporting enterprise reporting and dashboarding solutions using Tableau, Power BI, Spotfire, or similar platforms
Experience with CI/CD, Git-based workflows, deployment automation, and operational support activities
Strong troubleshooting, incident management, debugging, optimisation, and production support capabilities
Good communication, collaboration, ownership, and technical leadership skills
Preferred Qualifications
Experience with Terraform, Dev Ops, and automation frameworks
Exposure to AI/ML, GenAI, LLMs, vector databases, or AI-enabled analytics solutions
Familiarity with AI-ready data platforms, RAG architectures, intelligent automation, or cloud AI services
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