Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Senior Data Analyst

Proactiviti
Cambridge
2 weeks ago
Applications closed

Related Jobs

View all jobs

Senior Data Analyst

Senior Data Analyst - SQL & Python

Senior Data Analyst - Electronics Engineering Manufacturing

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Careers.
Be your best and authentic self.If you are excited to work on impactful projects with interesting and diverse clients in North America, Canada, Asia, and Europe - then this might just be the place for you.

Proactiviti offers competitive compensation, training, and hardware and software.

Salary & Benefits

Our clients in North America and Asia.

Who you'll work with

Data Analysis and Categorization

  1. Analyze existing data structures and identify key data elements for migration.
  2. Categorize and map data from legacy systems to target systems, ensuring compatibility and consistency.
  3. Collaborate with stakeholders to understand data migration requirements and address any challenges.

Data Cleansing and Quality Assurance

  1. Conduct data cleansing activities to ensure that the data being migrated is accurate, complete, and free of duplicates.
  2. Implement data quality checks and validation processes to ensure the integrity of migrated data.
  3. Identify and resolve discrepancies or issues in the data to ensure a smooth migration process.

Data Migration Execution

  1. Plan and execute data migration processes, ensuring minimal disruption to business operations.
  2. Collaborate with IT and development teams to configure and test data migration tools and scripts.
  3. Monitor and report on the progress of data migration, ensuring that all milestones are met and any issues are promptly addressed.

Data Warehouse Analysis and Design

  1. Analyze and document existing data warehouse models to map meanings and definitions with source systems.
  2. Document dimensions, attributes, hierarchies, and metrics from business reporting requirements.
  3. Analyze data extraction, transformation, and loading code (ETL pipelines) to understand the meaning of transformation and data treatment rules and reverse engineer those into requirements.
  4. Analyze data using SQL and Power BI.

Customer-Facing Communication

  1. Act as the primary point of contact between the vendor and the end customer during the migration process.
  2. Provide regular updates to customers on the status of the migration and any potential risks or delays.
  3. Address customer queries and concerns, ensuring a positive and collaborative relationship throughout the project.

Documentation and Reporting

  1. Create and deliver reports on the success and outcomes of data migrations, highlighting any key findings or issues.
  2. Document lessons learned from each migration project to inform future migrations and improve processes.

Stakeholder Management

  1. Work closely with internal teams, vendors, and end customers to ensure alignment and clarity on data migration objectives.
  2. Facilitate meetings and discussions to gather requirements, address challenges, and ensure smooth collaboration between all parties.
  3. Develop and maintain comprehensive documentation of data migration processes, including mappings, transformation rules, and data quality checks.
  4. Escalate any risks or challenges to management in a timely manner, with recommendations for resolution.

What you'll do

  1. Bachelor’s degree in Information Technology, Data Management, or a related field, or equivalent experience.
  2. 4+ years of experience in data migration, data analysis, or data management.
  3. C1 (Cambridge) equivalent in English (spoken comprehension, and writing) - Business Professional.
  4. Proficiency in data migration tools and techniques, including ETL (Extract, Transform, Load) processes.
  5. Strong analytical and problem-solving skills, with the ability to manage complex data sets and ensure data integrity.
  6. Excellent verbal and written English language skills, with the ability to communicate effectively with customers and vendors.
  7. Experience with database management systems such as SQL Server, Oracle, or MySQL.
  8. Familiarity with data mapping, cleansing, and validation techniques.
  9. Strong organizational skills and attention to detail, with the ability to manage multiple projects simultaneously.
  10. Relational databases, SQL, structured, semi-structured, and unstructured data formats.

Preferred Qualifications:

  1. Master Data Management experience (customer matching).
  2. Data Integration design and development (ETL, API, Messaging).
  3. Telecommunications industry and data experience.
  4. Experience in customer-facing roles, particularly in data migration or system implementation projects.
  5. Knowledge of cloud-based migration tools and processes.
  6. Familiarity with Agile/Scrum methodologies.
  7. Development of dashboards and reports in Power BI.

QualificationsRole Description

In this role, you will ensure data consistency, accuracy, and completeness throughout the migration process as well as the analysis of requirements for the data warehouse. The ideal candidate will have excellent English language skills, be customer-facing, and act as a bridge between the vendor and the end customer. This is a dynamic role requiring strong analytical skills, attention to detail, and the ability to manage stakeholder relationships effectively.


#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.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.