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Senior Data Engineer

About the Role

  1. Building Scalable Data Infrastructure

As our Senior Data Engineer, you will be the foundation of our data team, designing and implementing scalable, secure, and well-structured data infrastructure. You will lead the adoption of best practices in data engineering, ensuring our pipelines—both real-time and incremental streaming—are efficient, resilient, and aligned with the needs of analysts, data scientists, and external customers.

  1. Leading Data Governance & Best Practices

You will establish and maintain our data catalogs, governance frameworks, and best practices for managing and transforming data. Your expertise in Databricks, Unity Catalog, Terraform (AWS + Databricks), Docker, Python, and DBT or flypipe will drive the team's success. Your role extends beyond just infrastructure-you will ensure that data accessibility, security, and documentation empower analysts and data scientists to extract maximum value.

  1. Hands-On Technical Leadership

We are looking for a hands-on leader who thrives on challenges. As we build our data engineering team, you will mentor and guide engineers in creating high-performance streaming and batch pipelines, optimizing transformations. Your willingness to challenge the status quo, experiment with new technologies, and foster a culture of innovation will be key to our success.

About the Team

  1. Small but High-Impact Team

We are a lean, highly technical data team with a strong foundation. As we build our data infrastructure from the ground up, we focus on practical, hands-on execution while ensuring our work aligns with both internal and customer needs. Every team member plays a crucial role in shaping the future of data at the company.

  1. Collaborative & Cross-Functional

Our team operates at the intersection of technology, product, and business strategy. The Director of Data is hands-on, ensuring the team has the right toolset and direction. The Product Manager connects data initiatives with business goals, while our Data Scientist is focused on building ML models to enhance our product offerings. We work side by side to ensure data is actionable, scalable, and impactful.

  1. Building from Scratch, Together

Right now, we’re in build mode—laying the foundation for a scalable, well-governed data ecosystem. This means a lot of coding, experimentation, and defining best practices. As we grow, we are looking for team players who are eager to shape this infrastructure, challenge assumptions, and help us build a best-in-class data environment.

What You’ll Do

Within 3 months, you will:

  • Assess & Prioritize Data Needs – Identify key data sources for ingestion, evaluate infrastructure security, setup, and scalability, and define a roadmap for implementation.

  • Improve Data Pipelines & Governance – Ensure pipelines, catalogs, and dictionaries are structured, well-documented, and support both internal and external dashboard accessibility.

  • Identify & Solve Technical Challenges – Quickly raise concerns, propose and implement technical solutions to improve data reliability, performance, and security.

  • Engage with Key Stakeholders – Build strong relationships with engineering and product teams to align data infrastructure with business needs and ensure smooth collaboration.

Within 6 months, you will:

  • Own the Technical Strategy for Data Engineering – Take full ownership of data infrastructure, pipelines, and governance, ensuring they are secure, scalable, and aligned with best practices.

  • Lead & Mentor the Data Engineering Team – Guide other data engineers, establish coding standards, and drive technical excellence in pipeline development, modeling, and transformation.

  • Adapt Data Systems to Business & Product Needs – Stay informed about product and business changes, proactively adjusting data models and infrastructure to support evolving requirements.

  • Optimize Performance & Cost Efficiency – Continuously refine data pipelines and modeling to balance cost, performance, and scalability, ensuring smooth workflows for data scientists and analysts.

Within 9 months, you will:

  • Own & Drive Data Engineering Strategy – Oversee the technical direction of data engineering, ensuring infrastructure, pipelines, and models evolve based on how users interact with data.

  • Optimize Costs & Performance Continuously – Implement cost-effective solutions while maintaining high-performance pipelines and scalable infrastructure for analytics and ML workloads.

  • Ensure Reliable & Well-Structured Data Models – Maintain and refine data models and engineering toolsets to provide data scientists, analysts, and engineers with the right foundation for their work.

  • Scale the Team & Maintain Data Operations – Lead onboarding and mentorship of new team members, ensuring smooth development and production operations while adapting to new data sources and evolving business needs.

What You’ll Bring

  • 5+ years as data engineer, working in a fast-paced environment, preferably in Fintech

  • Expertise in Data Infrastructure & Pipelines – Strong experience in building, optimizing, and maintaining data pipelines (incremental & real-time/streaming) with a focus on scalability, security, and performance.

  • Deep Knowledge of Databricks & Unity Catalog – Hands-on experience with Databricks, Unity Catalog, and data governance best practices for secure and organized data access (Databricks Data engineer Professional certificate is a plus!)

  • Proficiency in Terraform & Cloud Infrastructure (AWS) – Strong ability to manage Databricks + AWS setup using Terraform, ensuring a scalable and well-managed infrastructure.

  • Strong Python & DBT Skills – Extensive experience in Python for data engineering tasks and DBT for transformation and data modeling.

  • Data Modeling & Governance Expertise – Proven ability to design efficient data models, implement data dictionaries/catalogs, and ensure high-quality, accessible data for analysts and data scientists.

  • Ability to Guide & Mentor Data Engineers – Leadership in setting best practices, reviewing code, and mentoring engineers, ensuring a high technical standard within the team.

  • Familiarity with Docker & CI/CD Pipelines – Experience with containerization and CI/CD for managing data engineering workflows.

  • Experience with External Data Sharing & Security – Understanding of data security and access management for both internal and external stakeholders.

  • ML Pipeline Deployment Knowledge – Understanding of ML models in production and how to optimize pipelines for data scientists.

Please note that this role may evolve as our business needs change, so we appreciate your flexibility and adaptability.

What’s In It For You?

  • Remote Flexibility: Enjoy the freedom of remote work from anywhere, balancing life and career seamlessly.

  • Unforgettable Off-Sites: Twice a year, bond with colleagues in exciting destinations, fostering teamwork and fresh ideas.

  • Paid Time Off and National Holidays: Enjoy 20 PTO days yearly and the National Holidays for relaxation and rejuvenation.

  • Stock Options: Joining us means having a stake in our success, so you'll receive stock options as part of your compensation package.

  • Home Office Setup: Create your ideal workspace with a dedicated budget for home office essentials.

  • Work Trip Budget: Grow personally and professionally with a budget for work-related trips and co-working.

About Us

Clutch is a revolutionary vertical SaaS company, proudly backed by Andreessen Horowitz (A16z), aimed at revolutionizing the way Credit Unions engage and change the lives of their members. As a champion of financial well-being, we address the urgent need for affordable lending solutions in an era where the average American grapples with over $155,000 in household debt.  Unlike traditional financial institutions, Clutch develops software to turn Credit Unions into FinTech lenders and leverage their balance sheets to responsibly lend to over 130M Americans. Our mission extends beyond mere financial transactions; we strive to fundamentally enhance the way credit unions interact with their members. By integrating cutting-edge technologies and user-centric designs, we help credit unions provide seamless digital experiences that are on par with leading tech companies. This approach not only preserves but revitalizes the longstanding tradition of community and member-focused service inherent to credit unions.

Please note: This position is offered on a contractor basis. Applicants must have the necessary documentation and authorization to work in the country where the job is located. Clutch cannot provide sponsorship or assist with obtaining work permits for this role.

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What You Should Know About Senior Data Engineer, Clutch

Are you ready to take your career to new heights as a Senior Data Engineer with Clutch? In this pivotal role, you will be the backbone of our data team, tasked with designing and implementing scalable and secure data infrastructure that meets our analysts', data scientists', and external clients' needs. At Clutch, you're not just building pipelines; you'll lead the charge in adopting best practices and maintaining a robust governance framework that ensures data accessibility and security. Your expertise in technologies like Databricks, Terraform, Docker, Python, and DBT will be key as you guide a small but impactful, cross-functional team in crafting a best-in-class data environment. With an emphasis on hands-on leadership and mentoring, you'll empower engineers to create high-performance streaming and batch pipelines while promoting a culture of innovation. This role is perfect for someone who thrives in a fast-paced environment and is keen to challenge the norm. If you want to help shape the future of data in a collaborative, fulfilling work environment while enjoying the benefits of remote working and a supportive company culture, then apply today and be a part of our journey at Clutch. Together, we will revolutionize the way credit unions engage with their members and change lives for the better.

Frequently Asked Questions (FAQs) for Senior Data Engineer Role at Clutch
What are the main responsibilities of a Senior Data Engineer at Clutch?

As a Senior Data Engineer at Clutch, you will be responsible for designing and implementing scalable, secure data infrastructure, ensuring efficient and resilient pipelines for both real-time and incremental streaming. You'll lead data governance efforts, maintain data catalogs, and establish best practices to empower analysts and data scientists. Mentoring junior engineers and optimizing data pipelines will also be crucial parts of your role.

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What qualifications are needed for the Senior Data Engineer position at Clutch?

To qualify for the Senior Data Engineer role at Clutch, you should possess at least 5 years of experience in data engineering, particularly in a fast-paced fintech environment. Strong expertise in Databricks, Terraform, Docker, Python, and DBT is essential, along with a deep understanding of data modeling, governance, and external data sharing practices. A strong ability to mentor and lead teams is also important.

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What technologies will I be working with as a Senior Data Engineer at Clutch?

In your role as a Senior Data Engineer at Clutch, you will utilize a variety of technologies such as Databricks, Unity Catalog, Terraform (AWS), Docker, Python, and DBT. Experience with ML pipelines and CI/CD processes is also beneficial, as you will work on building and optimizing data pipelines that support both internal and customer needs.

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What benefits does Clutch offer its Senior Data Engineer?

Clutch offers an impressive array of benefits for the Senior Data Engineer role, including remote work flexibility, generous paid time off (20 PTO days), stock options, and a budget for creating your ideal home office setup. You’ll also have opportunities for personal and professional growth through work trip budgets and unforgettable off-site bonding events.

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What is the team environment like at Clutch for a Senior Data Engineer?

The team environment for a Senior Data Engineer at Clutch is collaborative and cross-functional. You will work closely with product managers, data scientists, and other engineers, ensuring alignment of data initiatives with business goals, all while building from the ground up in an innovative, high-impact setting.

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Common Interview Questions for Senior Data Engineer
Can you explain the role of data governance in your past projects as a Senior Data Engineer?

In your response, highlight the significance of data governance in ensuring data quality, accessibility, and compliance with regulations. Share specific examples where you established data governance frameworks or protocols that improved data integrity and usability.

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Describe a challenging data pipeline you built and how you optimized it.

Prepare to walk interviewers through a detailed example, focusing on the initial challenges you faced, the solutions you implemented, and the performance improvements achieved. Use metrics to demonstrate the impact of your optimizations.

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How do you approach mentoring junior data engineers?

Your answer should reflect a supportive and engaging mentoring style. Discuss techniques you use to promote best practices, encourage continuous learning, and provide constructive feedback, along with any specific experiences that illustrate your approach.

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What is your experience with real-time data processing?

Detail your hands-on experience with real-time data processing technologies, including specific tools and languages used. Highlight any projects where real-time processing was critical to the business and how you ensured data reliability.

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How do you ensure data security in your engineering projects?

Focus your answer on the strategies you apply to manage data security risks, including governance practices, encryption techniques, access control measures, and your familiarity with relevant compliance standards, ensuring they are fully integrated into your engineering practices.

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What tools do you prefer for data modeling and why?

Be ready to discuss your preferred tools for data modeling, such as DBT, along with your reasoning. Highlight how these tools enable better structure, documentation, and optimization of data for analytics and machine learning.

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Can you describe a time you faced technical challenges in a data project and how you overcame them?

Use the STAR method (Situation, Task, Action, Result) to structure your answer, detailing a particular challenge you encountered and the methods you used to resolve it. Emphasize the outcome and any improvements resulting from your solutions.

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What best practices do you enforce for data pipeline development?

Discuss the importance of coding standards, documentation, and testing in your past experiences. Share specific best practices you advocate for, and provide examples of how they led to improved team collaboration and project outcomes.

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How do you stay current with new technologies and methodologies in data engineering?

Articulate your commitment to continuous learning by mentioning resources such as online courses, industry conferences, or communities you follow. Provide examples of how you’ve applied new technologies or methodologies successfully in past projects.

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What experience do you have with deploying machine learning models into production?

Outline your familiarity with ML pipelines, including deployment processes and the collaboration required between data engineers, data scientists, and stakeholders. If possible, mention specific models or platforms you've used in previous projects.

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DATE POSTED
March 23, 2025

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