Job Title: Technical Architect – Data Platform & Analytics (Life Sciences)
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 DevOps, CI/CD, operational
excellence, and AI-enabled data initiatives across enterprise environments.
Responsibilities and Duties
Job Description
• 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 DevOps, 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 MedTech domains
• Strong hands-on expertise in SQL, Python, PySpark, and cloud-native data engineering
technologies
Job Description
• 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, DevOps, 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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