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For Companies At a Glance
- Tasks: Lead a dynamic data engineering team and drive innovative data solutions.
- Company: Join a forward-thinking company focused on leveraging data for impactful decisions.
- Benefits: Enjoy flexible work options, professional development opportunities, and a collaborative culture.
- Why this job: Be part of a team that values creativity, growth, and making a real difference with data.
- Qualifications: Proven leadership in data engineering, strong AWS knowledge, and excellent communication skills required.
- Other info: Ideal for those passionate about technology and eager to shape the future of data.
The predicted salary is between 54000 - 84000 £ per year.
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
- 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.
- 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.
- 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.
- 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.
Locations
Data Engineering Manager employer: TalentHawk
Contact Detail:
TalentHawk Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineering Manager
✨Tip Number 1
Network with professionals in the data engineering field, especially those with experience in AWS. Attend industry meetups or webinars to connect with potential colleagues and learn about their experiences at companies like StudySmarter.
✨Tip Number 2
Showcase your leadership skills by sharing examples of how you've built and motivated teams in previous roles. Be prepared to discuss specific strategies you used to foster a high-performing environment during interviews.
✨Tip Number 3
Stay updated on the latest trends in data engineering, particularly around AWS services and DataOps methodologies. This knowledge will not only help you in interviews but also demonstrate your commitment to continuous learning.
✨Tip Number 4
Prepare to discuss how you have successfully managed delivery timelines and performance metrics in past projects. Highlight any innovative solutions you implemented to enhance operational efficiency, as this aligns with our goals at StudySmarter.
We think you need these skills to ace Data Engineering Manager
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in data engineering, particularly your leadership roles and technical skills related to AWS, Python, SQL, and pySpark. Use specific examples to demonstrate your achievements and impact.
Craft a Compelling Cover Letter: In your cover letter, express your passion for data engineering and how your vision aligns with the company's goals. Mention your experience in team building and stakeholder engagement, showcasing your people-first approach.
Showcase Technical Expertise: When detailing your technical skills, focus on your hands-on experience with AWS services and any relevant projects you've led. Highlight your understanding of CI/CD pipelines and DataOps methodologies if applicable.
Prepare for Potential Questions: Anticipate questions related to your leadership style, decision-making process, and how you manage operational excellence. Be ready to discuss how you foster a high-performing team and drive strategic vision in data engineering.
How to prepare for a job interview at TalentHawk
✨Showcase Your Technical Expertise
Be prepared to discuss your hands-on experience with AWS services and data engineering tools. Highlight specific projects where you've designed scalable data solutions or optimised data platforms, as this will demonstrate your technical leadership capabilities.
✨Emphasise People Management Skills
Since the role involves team building and coaching, share examples of how you've fostered a high-performing team. Discuss your approach to mentoring and how you've developed a leadership culture within your previous teams.
✨Demonstrate Strategic Vision
Articulate your understanding of how to shape a data engineering roadmap. Be ready to discuss how you've transformed strategic visions into actionable outcomes in past roles, ensuring alignment with organisational goals.
✨Prepare for Stakeholder Engagement Scenarios
Expect questions about how you engage with stakeholders and represent your team. Prepare examples that showcase your communication skills and your ability to advocate for data practices across different teams.
