Data Engineer

Ready to take the next step as a Data Engineer in Westbrook, Maine? Do you enjoy building scalable data pipelines, solving complex SQL challenges, and transforming raw data into trusted datasets that power analytics, machine learning, and AI-driven decision-making? If you're energized by modern cloud data platforms, collaborative engineering teams, and the opportunity to make a direct impact on data and AI initiatives, ProSearch is partnering with an innovative client to find a Data Engineer who can help deliver reliable, production-ready data solutions.

This hybrid opportunity is ideal for professionals with experience in SQL, data engineering, ETL/ELT development, data warehousing, Databricks, Snowflake, AWS, and dbt who enjoy building and enhancing data pipelines that support analytics, machine learning, and advanced research initiatives. You'll join a highly collaborative Data & AI team that values technical excellence, continuous learning, and teamwork.

The Opportunity

This position supports a high-performing Data & AI Center of Excellence, where you'll work alongside Data Scientists, Machine Learning Engineers, Analysts, and fellow Data Engineers to deliver high-quality data solutions that enable innovation.

Rather than building a platform from scratch, you'll focus on improving, modernizing, and scaling existing data processes. You'll take legacy scripts, manual workflows, and partially developed solutions and transform them into reliable, maintainable, and production-ready data pipelines.

This is an excellent opportunity for a Data Engineer with approximately 2-4 years of experience who wants hands-on exposure to modern cloud technologies, machine learning workflows, and enterprise-scale data engineering practices.

What You Will Do

Data Engineering & Pipeline Development

• Design, build, and maintain robust data pipelines that collect, transform, and deliver structured data
• Develop SQL-based transformation logic and pipeline workflows
• Support end-to-end ELT processes following modern data architecture patterns
• Contribute to data modeling, transformation, testing, deployment, and documentation
• Convert one-off scripts and manual processes into scalable, reusable solutions
• Build and maintain dbt models, tests, and pipeline components
• Monitor data quality and pipeline performance and proactively identify issues

Cloud Data Platforms & Analytics

• Develop pipelines within modern cloud platforms such as Databricks, Snowflake, AWS, or similar environments
• Work with structured and semi-structured data sources
• Support batch and streaming ingestion workflows
• Manage schema evolution and file ingestion processes involving Parquet and JSON formats
• Contribute to data lineage and governance practices

Collaboration & Delivery

• Partner closely with Machine Learning Engineers, Data Scientists, and Data Analysts
• Support data preparation for algorithm development and model training
• Participate in GitHub-based version control and CI/CD deployment workflows
• Follow established engineering standards, reusable patterns, and team best practices
• Contribute to a culture of documentation, collaboration, and continuous improvement

What We Are Looking For

Required Qualifications

• Bachelor's degree or equivalent combination of education and experience
• 2-4 years of experience in Data Engineering, Data Analytics, or a related technical field
• Advanced SQL skills with experience supporting data transformation and pipeline development
• Strong understanding of data warehousing concepts and ELT/ETL methodologies
• Experience building data pipelines on platforms such as Databricks, Snowflake, AWS, or similar cloud environments
• Experience with testing, deployment, data modeling, and documentation practices
• Experience using GitHub or similar version control tools
• Strong communication and collaboration skills

Preferred Qualifications

• Hands-on experience with dbt Core or dbt Cloud
• Experience with Spark SQL and Python
• Experience with streaming data pipelines and event-driven architectures
• Familiarity with schema evolution management
• Experience working with Parquet and JSON file formats
• Exposure to machine learning, analytics, or AI-related data workflows

What Success Looks Like

Successful candidates quickly become productive in SQL-based development, learn the team's data architecture and engineering standards, and contribute meaningful improvements to existing pipelines and workflows.

You'll develop reliable, scalable solutions that improve data quality, streamline processing, and enable Machine Learning Engineers and Data Scientists to build and train high-quality models with confidence.

Location Requirements

• Hybrid role based in Westbrook, Maine
• Approximately eight onsite days per month
• Tuesdays are the team's primary in-office collaboration day
• Local candidates only

Why This Role Stands Out

• Work on meaningful analytics and AI-driven projects
• Collaborate directly with Data Scientists and Machine Learning Engineers
• Gain exposure to Databricks, dbt, cloud data platforms, and modern data engineering practices
• Join a highly collaborative Data & AI organization
• Opportunity to modernize and improve enterprise data pipelines
• Hybrid work schedule with strong team engagement

Ready to Apply?

This role is ideal for a Data Engineer, Analytics Engineer, ETL Developer, SQL Developer, or Data Platform Engineer who enjoys solving complex data challenges, building reliable data pipelines, and working with modern cloud data technologies.

If you're a Data Engineer in Westbrook, Maine seeking an opportunity to develop your skills in SQL, Databricks, dbt, data warehousing, and machine learning data pipelines, we'd love to connect.

Apply through ProSearch today to start a confidential conversation about this exciting opportunity.

Compensation: $45-$60/hour

Salary Range Disclaimer: The salary range for this role reflects the minimum and maximum compensation for the role. Offers are typically made between the range minimum and the range midpoint. Actual compensation will be determined based on job-related skills, experience, and expertise, as evaluated during the interview process. Compensation may also vary based on where a role is performed.