Principal Data Architect, Consulting

Cognizant
Greater London
2 months ago
Applications closed

Related Jobs

View all jobs

Principal Architect_Data Engineer_4

Principal Data Engineer

Senior Data Engineer

Data Architect New York (Basé à London)

Data Architect New York (Basé à London)

Principal Data Engineer

The Company

Cognizant (NASDAQ:CTSH) is a leading provider of information technology, consulting, and business process outsourcing services, dedicated to helping the world's leading companies build stronger businesses. Headquartered in Teaneck, New Jersey (U.S.), Cognizant has over 350,000 employees as of January 2024. Cognizant is a member of the NASDAQ-100, the S&P 500, the Forbes Global 1000, and the Fortune 500 and is ranked among the top performing and fastest growing companies in the world.

Cognizant Consulting

At Cognizant, our consultants orchestrate the capabilities to truly change the game across strategy, design, technology and industry/functional knowledge to deliver insight at speed and solutions at scale. Our consulting services elevate the unique abilities and business aspirations of customers and employees and build relationships based on trust and value.

Role Overview

The Principal Data Architect will design and implement solutions using a range of AWS infrastructure, including S3, Redshift, Lambda, Step Functions, DynamoDB, AWS Glue, RDS, Athena, Kinesis, Quicksight. We also widely use ‘other’ tech such as Snowflake, DBT, Databricks, Informatica, Matillion, Airflow, Tableau, Power BI etc.

  • Transformational Leadership:Lead and guide the development of cloud data platforms, leveraging your extensive experience to drive innovation and change.
  • Effective Communication:Collaborate seamlessly with C-suite executives, translating business vision into actionable data initiatives that deliver tangible value.
  • Data Strategy Expertise:Shape robust data strategies that align with organizational goals, emphasizing business intelligence (BI) and AI/ML capabilities.
  • Team Building:Assemble and nurture high-performing data teams, fostering a culture of excellence and driving multi-million-pound benefits.
  • Solution Implementation:Successfully drive complex, large-scale data solutions from concept to execution.
  • Continuous Learning:Demonstrate a commitment to personal and team growth, staying abreast of industry trends and best practices.

Essential experience:

  • Proven track record in delivering transformative cloud data platforms.
  • Deep understanding of data strategy formulation and execution.
  • Ability to navigate complex programs and drive successful outcomes.
  • Shaping & designing solutions (notably data analytics, data integration, data platform)
  • Driving the performance of assurance activity to delivery appropriate quality
  • Leading the production of solutions in collaboration with Engagement Manager
  • Presenting public & client facing collateral about projects and practices
  • Designing & developing solutions (notably analytics, data integration, AI/ML)
  • Designing & developing data models aligned to the functional and non-functional requirements
  • Work closely with other members of agile deployment team
  • Selection & configuration of appropriate, base technologies (eg Amazon Redshift, RDS)
  • Selection & application of appropriate standards & principles
  • Capture & implementation of functional & non-functional requirements
  • Expert in data modelling and latest data trends including Data Warehouses, Data Lakes, 3NF, Dimensional Modelling, Data Mesh, Data Fabric
  • Supporting the identification of risks & issues
  • Undertaking appropriate assurance activity (eg design review, code walk-through, code test) to delivery appropriate quality in the technology solutions being produced
  • Harvesting valuable assets to support learning & effective re-use
  • Promotion of DevOps practices with both clients & internal delivery teams
  • Feed into development of the Data Management Practice
  • Supporting the shaping of engagements through contribution to production and operational estimates

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Jobs

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.

Jobs Careers

Data Science Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Data science has become one of the most sought‑after fields in technology, leveraging mathematics, statistics, machine learning, and programming to derive valuable insights from data. Organisations across every sector—finance, healthcare, retail, government—rely on data scientists to build predictive models, understand patterns, and shape strategy with data‑driven decisions. If you’re gearing up for a data science interview, expect a well‑rounded evaluation. Beyond statistics and algorithms, many roles also require data wrangling, visualisation, software engineering, and communication skills. Interviewers want to see if you can slice and dice messy datasets, design experiments, and scale ML models to production. In this guide, we’ll explore 30 real coding & system‑design questions commonly posed in data science interviews. You’ll find challenges ranging from algorithmic coding and statistical puzzle‑solving to the architectural side of building data science platforms in real‑world settings. By practising with these questions, you’ll gain the confidence and clarity needed to stand out among competitive candidates. And if you’re actively seeking data science opportunities in the UK, be sure to visit www.datascience-jobs.co.uk. It’s a comprehensive hub featuring junior, mid‑level, and senior data science vacancies—spanning start‑ups to FTSE 100 companies. Let’s dive into what you need to know.

Jobs

Negotiating Your Data Science Job Offer: Equity, Bonuses & Perks Explained

Data science has rapidly evolved from a niche specialty to a cornerstone of strategic decision-making in virtually every industry—from finance and healthcare to retail, entertainment, and AI research. As a mid‑senior data scientist, you’re not just running predictive models or generating dashboards; you’re shaping business strategy, product innovation, and customer experiences. This level of influence is why employers are increasingly offering compensation packages that go beyond a baseline salary. Yet, many professionals still tend to focus almost exclusively on base pay when negotiating a new role. This can be a costly oversight. Companies vying for data science talent—especially in the UK, where demand often outstrips supply—routinely offer equity, bonuses, flexible work options, and professional development funds in addition to salary. Recognising these opportunities and effectively negotiating them can have a substantial impact on your total earnings and long-term career satisfaction. This guide explores every facet of negotiating a data science job offer—from understanding equity structures and bonus schemes to weighing crucial perks like remote work and ongoing skill development. By the end, you’ll be well-equipped to secure a holistic package aligned with your market value, your life goals, and the tremendous impact you bring to any organisation.

🍪 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.