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Data Engineer

At DUAL North America, our core values dictate how we live and work. We are a group with independence and people at its heart and we are a home for talent with a unique culture: the biggest small company in the world.

The focus on being a People First business has always been at the very heart of the Group; Our vision was to create an independent business with a unique culture and one that would survive and thrive as a business controlled by the people working for it. And finding the most talented and entrepreneurial people to join the Group has been and will continue to be key.

DUAL North America, Inc. is seeking a

Data Engineer

Classification: Exempt
Division: Information Technology
Reports to: VP of Data Integration and Analytics
Travel: 10-15%
Work Environment: Hybrid/Remote, United States

Salary Range: $115,000.00-$125,000.00
 
About the role: 

DUAL North America is seeking a Data Engineer. We are a forward-thinking leader in the insurance industry, with a commitment to achieving our ambitious business goals by leveraging innovative data strategies. We are seeking a strategic and technical data and analytics leader to transform data into actionable information to ensure we meet our long-term goals.

Position Overview:

The Data Engineer will be a key team member in developing and delivering DUAL North America's data platform and reporting solutions. This role will help implement the data architecture and data platform roadmap that will enable analytics and reporting for the enterprise. The role requires technical skills to develop data engineering pipelines, identify and establish best practices for developing data solutions, and business acumen to deliver solutions further business objectives.

Role Responsibilities:

  • Data Modeling: Design and implement dimensional, relational, and 3NF data models. Develop conceptual, logical, and physical data models to support business requirements.

  • Data Pipelines: Develop and maintain ETL and ELT processes. Create batch, micro-batch, and low latency pipelines to integrate data systems efficiently.

  • Operational Data Store Development: Build near-real-time operational data stores. Design systems to enable operational reporting and application integration.

  • Data Lakehouse: Construct Medallion-like layers to support data lakehouse architecture. Empower enterprise BI, advanced analytics, and machine learning initiatives.

  • Development Lifecycle: Manage source control, branching, merging, and pull requests. Implement continuous integration and continuous deployment (CI/CD) practices. Conduct integration and unit testing to ensure data quality and reliability.

  • Tools and Technologies: Utilize Azure SQL Databases and Azure Data Factory. Develop solutions using Databricks (Spark development, Notebooks, R/Python/Scala). Work with iPaaS platforms (e.g., Mulesoft, Informatica) and other data ingestion tools.

Key Requirements:

  • Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field; Master’s degree or MBA preferred.

  • Extensive experience (5+ years) in data engineering, analytics, or similar data focused discipline.

  • Expertise in designing and developing data pipelines, modeling data to drive analytical insights across business teams and leadership.

  • Experience with cloud data architecture best practices and patterns for Cloud Data Warehouses, Data Lakehouse, Data Fabric, Data Mesh, and similar.

  • Experience with modern cloud environments like Azure, AWS, GCP (Azure preferred).

  • Strong programming skills with languages like SQL, Python, R, or similar languages.

  • Excellent communication and stakeholder management skills.

  • If you do not meet all the qualifications for this role, we still encourage you to apply, as we are always looking for diverse talent to join our growing team.

What we offer:
A career that you define. Yes, we offer all the usual rewards and benefits - including medical, dental, vision, a wide variety of wellbeing offers, competitive salary, unlimited PTO, 401k with company match, paid volunteer days and more.

We provide an environment where new ideas are encouraged and celebrated, where people who want to have a real hand in our success thrive. We want people who want to make a difference - not just in the workplace, but in the industry and in the wider community.

EEO Statement:
We consider our people our chief competitive advantage and as such we treat colleagues, candidates, clients, and business partners with equality, fairness, and respect. DUAL North America provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training. DUAL will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment.

Average salary estimate

$120000 / YEARLY (est.)
min
max
$115000K
$125000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Data Engineer, Hyperion Group

At DUAL North America, we're on the lookout for a passionate Data Engineer to join our innovative team! As a forward-thinking leader in the insurance sector, we believe that our strength lies in our people. This role, which can be performed from the comfort of your own home in Texas, is about transforming data into actionable insights that help us meet our ambitious business goals. As a Data Engineer at DUAL North America, you will dive deep into developing and delivering our data platform and reporting solutions. Your expertise in designing data architectures and engineering pipelines will be vital for integrating diverse data systems effectively. You’ll also get to work with cutting-edge technologies like Azure SQL Databases and Databricks to build operational data stores and support advanced analytics initiatives. With responsibilities ranging from data modeling to managing source control practices, this position is designed for someone who enjoys a hands-on yet strategic approach to data solutions. Join us, and you get to be part of a culture that celebrates new ideas and offers benefits like unlimited PTO, a comprehensive health plan, and a chance to make a real difference—not just in your role, but in the community and industry, too. Are you ready to be a part of the biggest small company in the world? Let's make an impact together at DUAL North America, where you define your career path!

Frequently Asked Questions (FAQs) for Data Engineer Role at Hyperion Group
What are the key responsibilities of a Data Engineer at DUAL North America?

As a Data Engineer at DUAL North America, you'll focus on developing data architectures and engineering pipelines that are integral to our data platform. Your responsibilities will include data modeling, creating and maintaining ETL processes, constructing operational data stores, and implementing CI/CD practices. These elements are crucial for facilitating advanced analytics and reporting throughout the company.

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What qualifications are necessary to become a Data Engineer at DUAL North America?

To qualify for the Data Engineer position at DUAL North America, candidates should have a Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field. While a Master’s degree or MBA is preferred, extensive experience (5+ years) in data engineering and proven expertise in cloud data architecture are essential for success in this role.

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What technologies should a Data Engineer be familiar with at DUAL North America?

At DUAL North America, Data Engineers should be well-versed in Azure SQL Databases and Azure Data Factory. Familiarity with tools like Databricks for development, along with programming skills in languages like SQL, Python, or R, will be key to effectively implement data solutions tailored to our business needs.

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Can I apply for the Data Engineer role at DUAL North America if I don't meet all the qualifications?

Yes! DUAL North America encourages candidates to apply for the Data Engineer role even if they do not meet all the qualifications. We believe in the value of diverse talent and backgrounds and are open to considering applicants who have relevant experience or show strong potential.

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What kind of work environment can Data Engineers expect at DUAL North America?

DUAL North America fosters a hybrid/remote work environment, allowing Data Engineers to work flexibly and effectively from home. We prioritize a people-first culture where new ideas are embraced, and team members are encouraged to contribute meaningfully to both the workplace and the wider community.

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Common Interview Questions for Data Engineer
What specific data engineering tools and technologies do you have experience with?

When answering this question, focus on the specific tools you are proficient in, such as Azure SQL Database, Azure Data Factory, or Databricks. Highlight not just your experience, but also how you've applied these technologies to solve real-world data challenges.

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Can you explain the process of creating an ETL pipeline?

In responding to this question, provide a step-by-step breakdown of your ETL process. Talk about data extraction, transformation based on business logic, and loading into the destination system. You can also mention any tools you've used and best practices you've followed to ensure data integrity and efficiency.

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How do you handle data quality issues in your engineering processes?

Discuss the methods you use to ensure data quality during the data pipeline management, like implementing validation checks, monitoring data lineage, or using data profiling tools. Emphasize how maintaining high data quality is critical for accurate analytics.

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What experience do you have with cloud data architecture?

Be prepared to discuss your experience in designing cloud data architectures. Talk about specific projects where you've worked with cloud platforms like Azure, AWS, or GCP, and detail the architectures you designed and the outcomes achieved.

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Describe a challenging data project you've worked on and how you overcame obstacles.

Use the STAR method (Situation, Task, Action, Result) to describe a relevant project. Highlight the complexities you faced, the steps you took to address them, and the positive results that came from your efforts.

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How do you prioritize tasks in a fast-paced environment?

Share your strategies for managing your workload effectively, such as using project management tools, setting clear deadlines, and regularly communicating with team members to align on priorities. It’s important to emphasize adaptability and effective communication.

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What is your experience working with stakeholders to gather data requirements?

Discuss your approach to collaborating with stakeholders, including how you facilitate discussions and gather comprehensive data requirements. Provide examples of how understanding business needs impacted your data engineering decisions.

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How do you approach documentation in data engineering?

Documentation is crucial in data engineering. Share your philosophy on maintaining clear, concise documentation that allows both technical and non-technical team members to understand your work. Illustrate this with examples of documentation processes you've implemented.

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Explain the difference between ETL and ELT.

Highlight that ETL (Extract, Transform, Load) means data is transformed before being loaded into the data warehouse, while ELT (Extract, Load, Transform) means loading raw data first and then transforming it as needed. Provide insights on when to use each based on specific project requirements you've encountered.

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What strategies do you use for successful data modeling?

Explain your approach to data modeling, such as understanding business requirements, creating both high-level and detailed models, and collaborating with stakeholders. Be sure to mention tools or methodologies you use, like normalization or dimensional modeling, to create efficient and effective data models.

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DATE POSTED
March 27, 2025

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