Technical Architect - Data Science

TESTQ Technologies Limited
Leicester
4 months ago
Applications closed

Related Jobs

View all jobs

Data Architect

Data Architect

Data Architect

Senior Data Scientist - Hertfordshire, HP2 4YL

Data Governance Lead

Technical Data Architect

TQUKI0480_4937 - Technical Architect - Data Science

Job Type: Permanent


Work Mode: Remote


Job title: Technical Architect - Data Science


Job Purpose:


TESTQ Technologies is an IT services and Solutions Company whose offerings span over a variety of industry sectors with strong technical, domain, and processexpertisehelping clients grow their businesses and decrease operational costs on a continuous basis in an ever-changing business environment.


The Technical Architect – Data Science is responsible for designing, developing, and implementing end-to-end data and AI solutions. This role bridges data engineering, data science, and architecture by defining scalable frameworks, guiding model deployment, and ensuring optimal use of cloud and big data technologies.


Job Description (Main Duties and Responsibilities):



  • Design and architect for end-to-end data science and AI solutions aligned with enterprise strategy.
  • Define scalable data architectures for ingestion, processing, storage, and analytics.
  • Lead the design of machine learning pipelines, model deployment frameworks, and MLOps solutions.
  • Collaborate with data scientists, engineers, and analysts to operationalize ML models in production.
  • Evaluate and recommend tools, frameworks, and best practices for data science and AI initiatives.
  • Ensure compliance with data governance, security, and privacy standards.
  • Provide technical leadership and mentorship to the data science and engineering teams.
  • Optimize cloud and on-premises data architectures for performance, cost, and scalability.
  • Drive innovation through proof-of-concepts (POCs) and pilot implementations of emerging AI/ML technologies.

Key Skills, Qualifications and Experience Needed [The candidate must demonstrate these in all stages of assessment]



  • A bachelor's degree in computer science, Information Technology, or related discipline.
  • 3 to 4 years of professional experience in Technical Architect – Data Science roles.
  • Should have strong proficiency in programming and scripting languages such as Python, R, SQL, Java, Scala, and Shell scripting.
  • They should be adept at using data science and machine learning libraries including NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras, XGBoost, and LightGBM for building and deploying advanced analytical models.
  • A solid understanding of data engineering and big data ecosystems is essential, with hands-on experience using Apache Airflow, Luigi, and dbt for data workflow orchestration, and familiarity with Hadoop, Spark, Hive, Kafka, and Flink for distributed data processing.
  • Expertise in working with both relational and NoSQL databases such as PostgreSQL, MySQL, Oracle, MongoDB, Cassandra, and Redis is required, along with experience in managing data lakes and data warehouses like Snowflake, Databricks, Amazon Redshift, Google BigQuery, and Azure Synapse.
  • The architect should have deep experience with cloud platforms—including AWS (S3, Glue, SageMaker, EMR, Lambda), Microsoft Azure (Data Lake, Synapse, ML Studio, Databricks), and Google Cloud Platform (BigQuery, Vertex AI, Dataflow, AI Platform)—and the ability to design scalable, cloud-native data solutions.
  • Proficiency in MLOps and DevOps tools such as MLflow, Kubeflow, DVC, and TensorFlow Extended (TFX) is required to enable model lifecycle management.
  • Knowledge of CI/CD pipelines using tools like Jenkins, GitHub Actions, Azure DevOps, or CircleCI, and experience with containerization and orchestration through Docker, Kubernetes, and Helm, is highly desirable. Familiarity with model monitoring and governance tools such as Evidently AI, WhyLabs, and Neptune.ai will be advantageous.
  • The role also requires expertise in data visualization and business intelligence tools including Power BI, Tableau, Looker, Superset, Plotly, and Dash for translating analytical insights into actionable business intelligence.
  • Additionally, strong understanding of API design and integration (REST, GraphQL), version control systems (Git, GitLab), and data security and compliance frameworks such as GDPR and HIPAA is important.

Qualifications: Bachelor's degree or above in the UK or Equivalent.


Salary: GBP 55,000 to GBP 65,000 per annum


Published Date: 03 November 2025


Closing Date: 02 December 2025


Evaluation: CV Review, Technical Test, Personal and Technical Interview and References


Job Type: Full-time, Permanent [Part time and Fixed Term option is available]


#J-18808-Ljbffr

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.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.