Data Architect

Cognizant
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Engineering Director

Data Engineering Director

GCP Data Architect & Big Data Engineer

Lead Data Engineer / Architect – Databricks Active - SC Cleared

Lead Data Engineer / Architect – Databricks Active - SC Cleared

Senior Data Engineer & ETL Architect (Hybrid)

Please note that this role requires security clearance at SC level. You must be SC cleared to be considered for the role.


Tasks and Responsibilities


Design:

Define Data Platform technical architecture by analyzing the requirements.

Define technical design for ingestion, visualization and integration

Analyze the data mapping and data models and come up with ingestion level data mapping

Coordinate and support dev and testing team

Data migration design

Define and execute data migration

Implement POC with limited developer support

Provide expert advice to product teams on best way to use existing services and utilities from the repository

Design and lead the Azure Devops implementation for data platform

Define logical and physical data models

Lead:


Lead the development team in best practice Devops processes:

Engage in and improve the whole lifecycle of services—from inception and design, through deployment, operation and refinement

Coaching and mentoring the product team engineers and developers on DevOps practices

Collaborate with the shared platform teams/architecture team and a wider community internally and externally


Operational reliability:

Act as the technical point of contact on the platform for the cloud service providers (e.g. Azure) to ensure the Platform is operational and is able to meet the service levels of the products using the platform

Support services before they go live through activities such as system design consulting, developing software platforms and frameworks, capacity planning and launch reviews

Maintain services once they are live by measuring and monitoring availability, latency and overall system health

Scale systems sustainably through mechanisms like automation, and evolve systems by pushing for changes that improve reliability and velocity

Lead incident response and resolve issues on the platform

Continuously improving our security, failover, resilience and disaster recovery mechanisms

To work with solution architect , Lead Developers on the adoption of new technologies into the estate, ensuring that these are shared with other areas and that they align with organization standards etc

To support the team in identifying issues with code / deliverables

To champion and drive through alerting and monitoring requirements for the platform

Identify and execute pro-active actions to ensure continued stability and performance of the platform


Our ideal candidate

Strong experience in Designing and delivering Azure based data platform solutions ,technologies including

Azure data bricks,Azure Synapse Analytics

Azure MS SQL managed service/Azure postgreSQL

Azure ADF and Data bricks, Azure Functions,App Service ,Logic app,AKS,Azure app servie,Webapp

Good knowledge in real time streaming applications preferably with experienceKafka Real time messaging or Azure Stream Analytics / Event Hub.

Spark processing and performance tuning

File formats partitioning for e.g. Parquet,JSON,XML,CSV

Azure Devops,GIT hub actions

  1. Hands on experience in at least one of Python with knowledge of the others
  2. Experience in Data modeling

Experience of synchronous and asynchronous interface approaches

Experience of designing and developing systems using micro services architectural patterns

DevOps experience in implementing development, testing, release and deployment processes using DevOps processes.

Knowledge in data modeling (3NF/Dimensional modeling/Data Vault2)

Work experience in agile delivery

Able to provide comprehensive documentation

Able to set and manage realistic expectations for timescales, costs, benefits and measures for success

Able to lead, undertake and interpret technical analysis

Autonomy

Works under broad direction. Work is often self-initiated. Is fully responsible for meeting allocated technical and/or project/supervisory objectives. Establishes milestones and has a significant role in the assignment of tasks and/or responsibilities.

Influence

Influences organization, customers, suppliers, partners and peers on the contribution of own specialism. Builds appropriate and effective business relationships. Makes decisions, which affect the success of assigned work, i.e. results, deadlines and budget. Has significant influence over the allocation and management of resources appropriate to given assignments.

Complexity

performs an extensive range and variety of complex technical and/or professional work activities. Undertakes work, which requires the application of fundamental principles in a wide and often unpredictable range of contexts. Understands the relationship between own specialism and wider customer/organizational requirements.


Nice to Have

  1. Experience with integration and implementation of data cataloging tool like Azure Purview,Unity Catalog
  2. Experience in implementing and integrating visualizations tools like Power BI/Tableau etc
  3. Snowflake knowledge

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Careers Jobs

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

Jobs

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Education Jobs

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.