Data Engineer

Storm4 have an extensive portfolio of experienced Data Engineers who strive to promote data excellence within the business and customer base

Storm4 partners with experienced Data Engineers who build the foundations behind high-performing CleanTech platforms. These are engineers who don’t just move data. They make it reliable, resilient, and ready for growth.

Whether you’re hiring a Data Engineer to strengthen your data infrastructure or you’re an engineer ready for your next CleanTech challenge, Storm4 connects innovative Energy, Infrastructure, and Mobility companies with talent that turns data into advantage.

These professionals bring world-class technical expertise, enabling organizations to unlock the value of data transforming early-stage startups into global CleanTech leaders through robust, scalable, and reliable data platforms.

What Is A Data Engineer?

A Data Engineer is responsible for designing, building, and maintaining the data infrastructure that allows organizations to collect, process, and analyze large volumes of data efficiently.

Working closely with Data Scientists, Product Teams, Analytics, and Engineering, Data Engineers ensure that raw data is transformed into high-quality, accessible, and reliable datasets that support decision-making across the business.

In Energy & Infrastrastructure environments such as grid optimization platforms, EV charging networks, and energy storage analytics, Data Engineers play a critical role in managing real-time data streams, optimizing performance, and ensuring systems can scale as adoption increases.

A successful Data Engineer combines strong software engineering fundamentals with deep data expertise, aligning all technical work with business objectives and sustainability outcomes.

Key Data Engineer responsibilities include:

Design, build, and maintain scalable data architectures aligned with business goals.
Develop, test, and optimize data pipelines (ETL / ELT) to ensure data reliability and quality.
Work with structured and unstructured data from multiple sources.
Implement monitoring to ensure pipeline stability and performance.
Optimize data storage, retrieval, and processing efficiency.
Collaborate with analytics, product, and machine learning teams to support insights and models.
Identify patterns, trends, and opportunities within large datasets.
Ensure data governance, security, and compliance best practices are followed.
Stay current with industry trends, tools, and emerging technologies.

Data Engineer Skills Requirements

Data Engineers require a strong foundation in software development, data systems, and cloud infrastructure.

Marketing & Technical Skills

Proficiency in SQL and at least one programming language such as Python, Java, or Scala.
Experience designing and maintaining data pipelines and workflows.
Strong understanding of ETL/ELT frameworks, batch and streaming data processing.
Experience with cloud platforms such as AWS, GCP, or Azure.
Familiarity with data warehousing solutions (Snowflake, BigQuery, Redshift).
Experience working with large-scale datasets and distributed systems.

Collaboration & Problem-Solving Skills

Strong analytical and problem-solving mindset.
Ability to work independently on complex challenges while collaborating cross-functionally.
Clear communication skills to translate technical concepts to non-technical stakeholders.
Attention to detail and commitment to data accuracy and reliability.
Ability to juggle multiple priorities in fast-moving environments.

Data Engineer Experience Requirements

Energy & Infrastructure organizations typically seek Data Engineer candidates with:

8+ years of experience in data engineering, software engineering, or analytics-focused roles.
Hands-on experience building and maintaining production-grade data pipelines.
Experience supporting analytics, reporting, or machine learning teams.
Exposure to CleanTech, EnergyTech, Infrastructure, or data-intensive SaaS environments is advantageous.
Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related discipline.

The Data Engineer role spans mid-level through senior and principal positions, depending on organizational needs and data maturity. In CleanTech organizations, Data Engineers are often central to product performance, operational efficiency, and AI-driven insights.

Data Engineer Salary Expectations

Data Engineers are among the most in-demand technical professionals in the CleanTech sector, with compensation reflecting their critical role in enabling data-driven innovation.

While salary levels for Data Engineers in the Clean Energy, Infrastructure or eMobility spaces can vary depending on company size, funding stage, and geographic location the average mid-level Data Engineer can expect to receive an annual salary somewhere in the range of $130,000 – $160,000 – Going up to $225,000+ for senior and lead data engineering positions.

If you want to find out the breakdown of Data Engineer salary expectations by sector and location, download our latest US CleanTech Salary Guides for a more in-depth look at salaries.

Download Salary Guides

Data Engineer Organization Hierarchy

Data Engineers typically sit within the data, engineering, or platform teams, depending on company structure.

Senior and Principal Data Engineers often influence architectural decisions across the wider organization.

Data Engineers Reports To:

Head of Data / Data Engineering Manager / Director of Engineering

Works Closely With:

Example Data Engineer Job Advert

Here is an example of how a compelling Data Engineer job advert might look for a CleanTech organization:

📊 Role: Data Engineer
💼 Industry: CleanTech | EV Charging & Energy Analytics
🌎 Location: Remote (U.S.-based)
💰 Salary: $155,000–$185,000 + Bonus + Equity

About the Company:
Our client is a fast-growing CleanTech company building intelligent EV charging and energy optimization platforms. As data volumes scale rapidly, they are seeking a Data Engineer to help design, maintain, and optimize their data infrastructure.

Responsibilities:

Build and maintain scalable data pipelines and architectures.
Ensure data quality, reliability, and availability across systems.
Support analytics and machine learning initiatives with clean, accessible datasets.
Monitor pipeline performance and resolve data issues proactively.
Collaborate with product and engineering teams to align data with business goals.

Requirements:

4+ years of experience in data engineering or similar roles.
Strong SQL and Python skills.
Experience with cloud data platforms and modern data stacks.
Exposure to CleanTech, energy, or large-scale data environments preferred.

📧 To apply: Send your resume to recruiter@cleantech-org.com or click “Apply” to start the conversation.

Data Engineer Hiring Process

Hiring a Data Engineer focuses on both technical capability and problem-solving mindset.
A typical hiring process includes:

Initial Consultation: Define data challenges, tooling, and success criteria.
Profile Sourcing: Identify candidates with relevant data and cloud experience.
Technical Screening: Assess SQL, programming, and data pipeline expertise.
Practical Assessment: Data modeling, pipeline design, or take-home exercise.
Team Interview: Evaluate collaboration style and cross-functional communication.
Final Interview: Alignment on role scope, growth potential, and culture fit.

Speak to a consultant

Data Engineer Interview Questions

A well-rounded interview process helps reveal the candidate’s technical skills, strategic awareness, and interpersonal skills.

Technical Questions

How do you design a scalable data pipeline for high-volume, real-time data?
What steps do you take to ensure data quality and reliability?
How do you choose between batch and streaming data architectures?
What tools or frameworks do you prefer for ETL/ELT and why?

Industry-Specific Questions

What data challenges are unique to CleanTech or energy platforms?
How would you support real-time analytics for EV charging or grid data?
How do regulatory or compliance requirements influence data design?
How can data engineering accelerate sustainability outcomes?

Problem Solving & Collaboration Questions

Describe a time you debugged a complex data pipeline failure.
How do you work with stakeholders to clarify data requirements?
How do you prioritize data requests from multiple teams?
How do you document and communicate data architecture decisions?

Experience & Background Questions

What data project are you most proud of and why?
How has your experience prepared you for this role?
Describe your experience working with cloud data platforms.
What attracts you to data engineering within the CleanTech sector?

AI, Data and analytics roles

AI, Data & Analytics roles we hire for.

Chief Data Officer

Storm4 Has A Portfolio Of Experienced Chief Data Officers Who Strive To Promote AI And Data Excellence To Drive Successful Business Outcomes.

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Chief Data Scientist

Storm4 Has A Portfolio Of Experienced Chief Data Scientists Who Strive To Empower Business Intelligence Through The Value Of AI And Data.

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Director Of Analytics

Storm4 Has A Network Of Experienced Director Of Analytics’ Professionals Who Strive To Empower AI-Driven Data Excellence Both Within Their Company And Customer Base.

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Head Of Data Science

Storm4 Have An Array Of Head Of Data Science Leaders In Their Network Who Aim To Encapsulate An AI And Data-Driven World By Promoting Data Excellence In Business Strategy.

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