Qureos Mentor

FalconSmartIT

<div>Job Title:BA with Pharma and Ai
Location: Fosters City CA
Job Type: Onsite , FTE 
 
Job Description: 
 
We are looking for an AI Scientific Business Analyst who combines life sciences expertise with business analysis and AI product delivery. This role will partner with Product Managers, Business Leads, AI Engineers, and Data teams to define and deliver AI-powered solutions that support clinical research and drug development. The ideal candidate has a strong scientific background, understands clinical development processes, and can translate complex business needs into structured AI requirements that enable successful product delivery. 
 
Primary (Must have) Skills 
 
Bachelor's or Master's degree in Life Sciences, Health Sciences, Pharmacy, Biotechnology, Biomedical Sciences, or related discipline 
 
Academic or professional experience in:
<ul><li>Clinical Research</li>
<li>Clinical Development</li>
<li>Translational Science</li>
<li>Oncology or related therapeutic areas</li>
</ul><ul><li>Strong understanding of clinical development lifecycle and pharmaceutical development processes.</li>
<li>8+ years of Experience working within pharmaceutical R&amp;D organizations.</li>
<li>8+ years of Excellent business analysis and requirements gathering skills.</li>
<li>Ability to convert ambiguous business problems into structured functional requirements.</li>
<li>5+ years of Experience defining KPIs, success metrics, and measurable business outcomes.</li>
<li>Strong written and verbal communication skills.</li>
<li>Self-driven with ability to work independently.</li>
<li>Experience managing UAT activities and stakeholder communication.</li>
</ul>Key Responsibilities: 
 
<ul><li>Individual contributor partner with Product Manager, Business Lead, AI Leads, and Data Lead to define AI use cases, including problem framing, success criteria, and measurable outcomes.</li>
<li>Translate business needs into AI-ready requirements, including:</li>
<li>Input data expectations</li>
<li>Model outputs (predictions, insights, recommendations)</li>
<li>User interaction patterns (e.g., workflows, prompts, dashboards)</li>
<li>Apply foundational knowledge of clinical research and drug development to ensure AI use cases, requirements, and acceptance criteria accurately reflect scientific intent and business objectives</li>
<li>Act as the primary bridge between &quot;what's needed&quot; (business) and &quot;what's possible&quot; (technical) to ensure stakeholder alignment.</li>
<li>Leads requirements refinement, including current/future state analysis, requirements documentation, prioritization, feasibility alignment and stakeholder alignment.</li>
<li>Document user, functional and AI-specific requirements including data inputs, model output, user interaction workflows with acceptance criteria aligned to business outcomes and system behavior.</li>
<li>Owns User Acceptance Testing planning and execution.</li>
<li>Partners with Testing team to ensure requirements are testable and the solution is ready for release.</li> 
</ul> 
Deliverables: 
 
<ul><li>Update and maintain user stories and acceptance criteria in Jira/ADO.</li>
<li>Author formal User Requirements Specification (URS)/Functional Requirements Specification (FRS), when required</li>
<li>Partners with testing teams to ensure requirements are clear, unambiguous, and testable.</li>
<li>Produce current state and future state artifacts (process flows, data flows, impact summaries) as required to support solution design.</li>
<li>Lead or contribute to data specifications, source-to-target mappings, and AI input/output definitions in partnership with Data and AI teams</li>
<li>Develop User Acceptance Test (UAT) plan, scenarios, and test cases aligned to approved requirements.</li>
<li>Lead UAT, including coordination of test cycles, defect logging, and retesting.</li>
<li>Produce UAT test summary report supporting business acceptance and release readiness.</li>
<li>Provide regular status updates covering progress, risks, issues, and dependencies.</li>
<li>Document decisions, assumptions, and clarifications related to contracted scope.</li>
</ul>Communication Skills: 
 
<ul><li>Communicate effectively with internal and customer stakeholders</li>
<li>Communication approach: verbal, emails and instant messages</li>
</ul>Interpersonal Skills: 
 
<ul><li>Strong interpersonal skills to build and maintain productive relationships with team members</li>
<li>Provide constructive feedback during code reviews and be open to receiving feedback on your own code.</li>
</ul>Problem-Solving and Analytical Thinking:
<ul><li>Capability to troubleshoot and resolve issues efficiently.</li>
<li>Analytical mindset</li> 
</ul> 
Task/ Work Updates 
 
<ul><li>Prior experience in working on Agile/Scrum projects with exposure to tools like Jira/Azure DevOps</li>
<li>Provides regular updates, proactive and due diligent to carry out responsibilities</li>
<li>Successfully understand business priorities, clinical workflows, and AI product roadmap.</li>
<li>Partner with stakeholders to define and document high-quality AI use cases and requirements.</li>
<li>Deliver complete business analysis artifacts including User Stories, URS/FRS, process flows, and acceptance criteria.</li>
<li>Establish clear AI input/output definitions and success metrics for prioritized initiatives.</li>
<li>Successfully lead User Acceptance Testing and support multiple AI solution releases.</li>
<li>Improve collaboration between scientific stakeholders and AI engineering teams, reducing requirement ambiguity and delivery delays.</li>
<li>Contribute to the successful rollout of AI capabilities supporting clinical development and drug discovery initiatives.</li>
<li>Become a trusted business partner for AI-enabled scientific product development.</li>
</ul>Understanding of AI/ML concepts and Generative AI solutions
<ul><li>Experience with AI products, analytics platforms, or intelligent automation initiatives</li>
<li>Knowledge of prompt engineering, AI agents, or LLM-based workflows</li>
<li>Experience working in Agile and iterative product delivery</li>
<li>GxP / Computer System Validation (CSV) knowledge</li>
<li>Basic SQL for data validation and analysis</li>
<li>Basic Python for exploratory data analysis</li>
<li>Familiarity with Jira or Azure DevOps</li>
</ul>Work at the intersection of AI and Life Sciences, shaping the future of drug discovery and clinical development.
<ul><li>Partner with multidisciplinary teams including scientists, clinicians, AI engineers, and product leaders.</li>
<li>Contribute to cutting-edge Generative AI and Agentic AI initiatives with real-world impact in healthcare.</li>
<li>Gain exposure to modern AI technologies, data platforms, and cloud-based AI ecosystems.</li>
<li>Be part of a collaborative, innovation-driven environment that encourages continuous learning and professional growth.</li>
<li>Play a key role in delivering AI solutions that accelerate research, improve decision-making, and enhance patient outcomes.</li>
<li>Opportunity to influence the next generation of AI-powered scientific products within a global pharmaceutical ecosystem.</li></ul></div>

BA with Pharma and Ai

<div>Job Title : Site Reliability Engineer
Job Location : Hove, UK (Hybrid 3 days office)
Job Type : FTE 
 
Job Description: 
 
SRE will play a pivotal role in driving the modernization of IT operations by implementing observability practices and automating toil. This position requires a deep understanding of Site Reliability Engineering (SRE) principles, modern observability tools, and automation techniques to ensure scalability, reliability, and efficiency in IT systems. This role requires a strategic thinker with hands-on expertise who can lead modernization efforts while fostering a culture of reliability and innovation. 
 
Primary Responsibilities: 
 
<ul><li>Work closely with Product Engineering team and implement strategies for modernizing IT operations enhancing observability and toil reduction.</li>
<li>Architect and deploy observability platforms to monitor system health, performance, and reliability effectively.</li>
<li>Propose &amp; drive strategies for AI-driven alerting and proactive anomaly detection to reduce MTTD &amp; MTTR.</li>
<li>Develop and enforce SRE best practices, including Service Level Objectives (SLOs), Service Level Indicators (SLIs), and Error Budgets.</li>
<li>Establish &amp; create AIOPS roadmap for improving operational efficiency.</li>
<li>Lead efforts to automate repetitive tasks (toil) using scripting, orchestration tools, and AI/ML-based solutions.</li>
<li>Drive toil automation initiatives for automated incident responses &amp; self-healing automation for achieving autonomous operations.</li>
<li>Collaborate with cross-functional teams to ensure systems are scalable, resilient, and maintainable.</li>
<li>Drive incident management and root cause analysis processes through automation, ensuring continuous improvement to enable autonomous operations.</li>
<li>Partner with engineering, architecture, and product teams to enable shift-left engineering practices ensuring reliability.</li>
<li>Mentor and guide teams on adopting SRE principles and tools.</li>
<li>Advocate for a culture of reliability, automation, and continuous improvement across the organization.</li> 
</ul> 
Key Skills: 
 
<ul><li>Strong expertise in implementing Site Reliability Engineering (SRE) principles.</li>
<li>Advanced knowledge of establishing observability using tools Dynatrace &amp; Datadog (primary skills).</li>
<li>Proficiency in automation &amp; scripting using Python &amp; Ansible (primary skills).</li>
<li>Strong experience with cloud platforms AWS &amp; Azure (primary skills).</li>
<li>Solid understanding of containerization and orchestration tools like Docker and Kubernetes.</li>
<li>Proficiency in cloud native distributed systems &amp; microservices architecture.</li>
<li>Exposure to AI/ML techniques for predictive analytics and automated problem resolution.</li>
<li>Familiarity with CI/CD pipelines &amp; enabling automated release &amp; deployment engineering solutions.</li>
<li>Good to have experience with chaos engineering tools like Gremlin or Chaos Monkey and implementing automation frameworks for resilience tracking.</li>
<li>Ability to manage and prioritize multiple projects in a fast-paced environment.</li>
<li>Strong interpersonal and communication skills to work effectively across teams.</li>
<li>Excellent problem solving, analytical thinking, and adaptability.</li>
<li>Strategic mindset balancing engineering excellence with business priorities.</li>
</ul>Preferred Qualifications: 
 
<ul><li>12+ years of experience in IT operations, SRE, or DevOps roles.</li>
<li>Proven track record of SRE experience in implementing observability and automation solutions in large-scale environments.</li>
<li>Certifications in cloud platforms, observability tools &amp; other SRE related areas.</li></ul></div>

Site Reliability Engineer

<div>Job Title: Data Architect
Job Location: London, UK
Job Type: Permanent/ Contract 
 
 
Note on Snowflake
Candidates must have significant, hands-on Snowflake architecture experience including schema design, ingestion patterns, security configuration, and cost governance. Experience with other cloud warehouses (BigQuery, Redshift, Databricks) will be considered alongside demonstrable ability to operate at Snowflake depth quickly.
Data Architect
CAGM Engineering Project Compass
Accounts (RTL) Payments Engine FX Enterprise Data Platform 
 
About the Role
We are seeking a senior Data Architect to join the CAGM Global Markets) engineering organisation as part of Project Compass. This programme is delivering next-generation capabilities across Accounts (Real-Time Ledger), Payments Engine, and Foreign Exchange all of which generate, consume, and depend on high-quality, well-governed data at scale.
The Data Architect will own the end-to-end data architecture across CAGM, spanning Snowflake as the enterprise data warehouse and a landscape of in-house application databases (relational, time-series, document, and in-memory stores) that serve real-time operational workloads. You will define how data flows from source systems into the warehouse, how application databases are modelled and managed, and how data products are exposed to downstream consumers within and beyond .
This is a hands-on, delivery-focused role. You will work closely with Integration Architects, platform engineers, and domain product teams to translate business data requirements into durable, governed, and scalable data solutions.
Key Responsibilities
Data Architecture &amp; Strategy
<ul><li>Define and own the CAGM data architecture target state, covering the Snowflake enterprise data warehouse, application databases, and the data flows that connect them</li>
<li>Establish a unified data modelling standard across relational (PostgreSQL, Oracle), in-memory (Redis), time-series (TimescaleDB / InfluxDB), and document (MongoDB) stores used by CAGM applications</li>
<li>Design the data ingestion and movement architecture real-time CDC pipelines, batch ETL/ELT patterns, and event-driven feeds from the NATS messaging layer into Snowflake</li>
<li>Define data domain boundaries, ownership, and lineage standards aligned with Project Compass product domains (RTL, Payments, FX)</li>
<li>Produce and maintain authoritative data architecture artefacts: entity-relationship models, data flow diagrams, data dictionaries, and Architecture Decision Records (ADRs)</li>
</ul>Snowflake &amp; Data Warehouse
<ul><li>Lead the design and evolution of the Snowflake data warehouse, including schema design (Raw / Conformed / Consumption layers), virtual warehouse sizing, and cost governance</li>
<li>Define standards for data loading (Snowpipe, Streams &amp; Tasks, external stages), transformation (dbt patterns), and data sharing across business units</li>
<li>Establish Snowflake data access controls, row-level security, dynamic data masking, and PII governance in line with regulatory requirements (GDPR, BCBS 239)</li>
<li>Champion Snowflake best practices for performance tuning, clustering keys, materialised views, and query optimisation</li>
<li>Evaluate Snowflake-native capabilities (Snowpark, Cortex AI, Dynamic Tables) and recommend adoption where they accelerate CAGM data product delivery</li>
</ul>Application Database Architecture
<ul><li>Govern the application database landscape across CAGM reviewing schema designs, indexing strategies, and data lifecycle management across all in-house databases</li>
<li>Define patterns for operational data stores (ODS) that bridge real-time application databases and the analytical warehouse layer</li>
<li>Ensure consistency between transactional data models and their warehouse representations, minimising transformation complexity and maximising fidelity</li>
<li>Set standards for database change management, migration tooling (Liquibase / Flyway), and schema versioning across the CAGM application estate</li>
<li>Identify and remediate data quality issues at source, defining data contracts between application teams and downstream consumers</li>
</ul>Data Governance &amp; Quality
<ul><li>Define and implement data governance frameworks covering data ownership, stewardship, classification (PII, sensitive, public), and retention policies</li>
<li>Establish data lineage and cataloguing standards, working with tooling such as Apache Atlas, Collibra, or Snowflake Horizon Catalog</li>
<li>Design and enforce data quality rules and SLAs at ingestion, transformation, and consumption layers</li>
<li>Collaborate with the Risk and Compliance function to ensure CAGM data architecture meets BCBS 239 Risk Data Aggregation and Reporting requirements</li>
<li>Champion Master Data Management (MDM) principles for shared reference data (counterparty, instrument, currency) across CAGM domains</li>
</ul>AI, Analytics &amp; Data Products
<ul><li>Define the architecture for CAGM data products curated, well-documented datasets served to analytics, reporting, and AI/ML consumers</li>
<li>Design feature stores and data pipelines that support AI/ML model training and inference for use cases such as FX pricing, payment anomaly detection, and limit utilisation forecasting</li>
<li>Evaluate and integrate AI-assisted data tooling (AI-powered cataloguing, natural language querying, automated data quality) where it accelerates productivity</li>
<li>Partner with the Analytics Engineering team to establish dbt modelling standards, testing frameworks, and documentation practices</li>
</ul>Collaboration &amp; Leadership
<ul><li>Work hands-on across multiple CAGM product teams as a data authority, balancing strategic design with direct delivery contribution</li>
<li>Guide and mentor application engineers on data modelling, query optimisation, and data quality best practices</li>
<li>Engage senior stakeholders across Technology, Finance, Risk, and Operations to communicate data strategy, risks, and trade-offs</li>
<li>Facilitate data architecture working groups with platform, BI, and enterprise architecture teams to align on shared standards</li>
</ul>Core Technical Skills
Data Warehouse
Snowflake schema design, Snowpipe, Streams &amp; Tasks, Snowpark, dynamic data masking, cost governance
Transformation
dbt (data build tool) modelling layers, testing, documentation, incremental strategies
Application Databases
PostgreSQL, Oracle, Redis, MongoDB, TimescaleDB / InfluxDB schema design, indexing, replication
Data Integration
CDC (Debezium / Kafka Connect), ETL/ELT pipelines, NATS event feeds, AWS Glue, Apache Spark
Cloud Platform
AWS S3, RDS, Aurora, Redshift (migration context), Glue, Lake Formation, IAM, VPC
Data Governance
Data lineage, cataloguing (Apache Atlas / Collibra / Snowflake Horizon), GDPR, BCBS 239, MDM
Architecture Practice
ERDs, data flow diagrams, data contracts, ADRs, C4 modelling, domain-driven data design
AI / ML Data
Feature stores, ML pipeline data design, Snowflake Cortex AI, vector stores, LLM data patterns
Query &amp; Performance
SQL optimisation, clustering keys, partitioning, query profiling, cost-based tuning
CAGM Data Landscape
The Data Architect will work across the following technology landscape. Candidates should have direct experience with the majority of these platforms and the ability to define coherent architecture across heterogeneous stores:
Platform / Store
Primary Use in CAGM
Key Architecture Concerns
Snowflake
Enterprise data warehouse, analytics, reporting, data sharing
Layer design, ingestion patterns, security, cost governance
PostgreSQL
Transactional data ledger entries, client records, audit
Schema design, CDC, replication lag, index strategy
Oracle DB
Legacy core banking integration, reference data
Migration strategy, data contracts, schema versioning
Redis
Real-time caches FX rates, limit state, session data
Cache invalidation, persistence strategy, data consistency
MongoDB
Document stores client profiles, trade enrichment data
Schema evolution, aggregation pipelines, CDC integration
TimescaleDB
Time-series market data ticks, position history
Hypertable design, retention policies, compression
NATS JetStream
Event streaming payments, ledger events, FX confirmations
Event schema contracts, consumer group design, replay strategy
AWS S3 / Glue
Data lake staging, archival, batch ingestion into Snowflake
Partitioning, file format (Parquet/ORC), Lake Formation governance
Finance Domain Knowledge
Candidates should have hands-on data architecture experience in one or more of the following financial services domains:
Domain
Key Data Concepts
Real-Time Ledger
Double-entry accounting data models, event-sourced ledgers, real-time balance aggregation, reconciliation datasets
Payments Engine
Payment message data (ISO 20022 / SWIFT), settlement instructions, payment status lifecycle, fee and charge data
Foreign Exchange
Trade data models, rate feeds and time-series storage, position keeping, P&amp;L attribution data
Limit Management
Exposure data models, limit hierarchy, breach event data, real-time risk aggregation feeds
Client Onboarding
Client master data, KYC / AML data structures, account hierarchy, regulatory reporting feeds
Regulatory Reporting
BCBS 239 data lineage, EMIR / MiFID trade reporting data, data quality SLAs for regulatory submissions
Experience &amp; Profile
<ul><li>15+ years of progressive technology experience, with at least 5 years in senior data architecture roles</li>
<li>Deep, hands-on experience with Snowflake as an enterprise data warehouse ideally holding Snowflake SnowPro Core or Advanced: Architect certification</li>
<li>Proven track record of designing data architectures across heterogeneous application database landscapes in large financial institutions or fintech organisations</li>
<li>Demonstrated experience implementing data governance frameworks, lineage tooling, and data quality programmes at programme scale</li>
<li>Comfortable working hands-on writing dbt models, reviewing SQL, profiling queries while operating at senior stakeholder and architecture level</li>
<li>Experience with CDC-based real-time data pipelines and event-driven data integration patterns</li>
<li>Strong communicator able to convey complex data architecture decisions to both engineering teams and business stakeholders</li>
<li>Familiarity with AI/ML data architecture patterns (feature stores, vector databases, LLM data pipelines) is a strong advantage</li>
<li>AWS Solutions Architect or AWS Data Analytics certification is advantageous</li></ul></div>

Data Architect

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BA with Pharma and Ai

Site Reliability Engineer

Data Architect