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

Apply Now

Data Engineering Manager

TalentHawk
Portsmouth
16 hours ago
Applications closed

Related Jobs

View all jobs

Data Engineering Manager

Data Engineering Manager

Data Engineering Manager

Data Engineering Manager

Data Engineering Manager

Data Engineering Manager

We are seeking a Data Engineering Manager with a strong technical foundation, proven experience leading data engineering teams, and expertise in AWS platforms. This role demands a combination of operational management and strategic vision to drive the success of our data platforms and align with organizational goals.


Responsibilities

1. People Management

  • Team Building & Coaching:
  • Foster a high-performing data engineering team through coaching, mentoring, and professional growth opportunities.
  • Develop a leadership culture within the team, ensuring engagement and motivation.
  • Stakeholder Engagement:
  • Act as a visible advocate for data practices across teams.
  • Confidently represent the data team and step in for senior leadership as needed.


2. Technical Leadership

  • AWS Expertise:
  • Hands-on experience with AWS services, scalable data solutions, and pipeline design.
  • Strong coding skills in Python, SQL, and pySpark.
  • Optimize data platforms and enhance operational efficiency through innovative solutions.
  • Nice to Have:
  • Background in software delivery, with a solid grasp of CI/CD pipelines and DataOps methodologies.
  • Exposure to ML/AI implementations.


3. Process & Delivery Management

  • Operational Excellence:
  • Manage delivery timelines, performance metrics, and team operations effectively.
  • Support technology upgrades, evaluate new tools, and adopt emerging trends.
  • Strategic Vision:
  • Shape the data engineering roadmap and transform vision into actionable outcomes.
  • Collaborate across teams to ensure the data work delivers tangible business value.


4. Leadership Style

  • Attributes:
  • Trustworthy, collaborative, and detail-oriented.
  • Strong decision-making skills and a people-first approach.
  • Positive mindset with a commitment to continuous learning.


Key Qualifications

  • Proven experience in a technical leadership role within data engineering.
  • Strong technical fluency and a problem-solving mindset.
  • In-depth knowledge of AWS services and their practical implementation.
  • Excellent communication and stakeholder management skills.
  • Experience with performance metrics, delivery management, and team operations.

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 Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

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.