We are seeking an experienced and technically strong Data Engineer to design, build, and maintain reliable data infrastructure and pipelines that support business intelligence, analytics, reporting, machine learning, and operational decision-making.
This is a full-time position offering $100,000–$135,000 per year, depending on experience, technical expertise, location, and qualifications. Candidates with advanced experience in cloud data platforms, distributed data processing, data architecture, and modern data engineering technologies may be considered toward the upper end of the compensation range.
The ideal candidate is highly analytical, comfortable working with large and complex datasets, and capable of building scalable, secure, and reliable data solutions.
Key Responsibilities
- Design, develop, and maintain scalable data pipelines and ETL/ELT processes.
- Build and maintain data infrastructure supporting analytics, reporting, and business applications.
- Develop efficient data models, schemas, and data transformation processes.
- Integrate data from APIs, databases, SaaS platforms, applications, and other sources.
- Ensure data quality, accuracy, availability, and consistency.
- Optimize data pipelines and queries for performance and scalability.
- Work with cloud-based data platforms and storage systems.
- Collaborate with data scientists, analysts, software engineers, and business stakeholders.
- Implement monitoring, testing, logging, and alerting for data pipelines.
- Establish and maintain data governance, security, and access controls.
- Troubleshoot data-processing and pipeline issues and identify opportunities for automation.
- Document data architecture, pipelines, processes, and technical standards.
- Help develop and maintain data engineering best practices across the organization.
- Evaluate new technologies and recommend solutions that improve data reliability and efficiency.
Qualifications
Required
- 3+ years of professional experience in data engineering or a related technical field.
- Strong proficiency in SQL and Python.
- Experience designing and maintaining ETL/ELT pipelines.
- Experience with relational and/or cloud-based databases.
- Strong understanding of data modeling, database design, and data warehousing.
- Experience working with APIs and integrating multiple data sources.
- Strong analytical and problem-solving skills.
- Ability to communicate technical concepts effectively to both technical and non-technical stakeholders.
Preferred
- Experience with AWS, Microsoft Azure, or Google Cloud Platform.
- Experience with Snowflake, Databricks, BigQuery, Redshift, or similar platforms.
- Experience with Apache Spark, Airflow, dbt, Kafka, or similar technologies.
- Experience with data lakes and modern data warehouse architectures.
- Experience with Docker, Kubernetes, or CI/CD environments.
- Familiarity with data governance, security, and privacy requirements.
- Experience supporting machine-learning or AI data pipelines.
- Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field is preferred but not required with equivalent professional experience.
Compensation & Benefits
The compensation for this position is $100,000–$135,000 per year, depending on experience, technical skills, cloud-platform expertise, and overall qualifications.
Candidates with advanced data-engineering experience, particularly with cloud platforms, distributed processing, modern data warehouses, and large-scale data pipelines, may be considered at the upper end of the range.
Additional benefits may include:
- Health, dental, and vision insurance
- Paid time off
- Paid holidays
- 401(k) or retirement plan
- Remote or hybrid work flexibility
- Professional development and training
- Technology and equipment allowance
- Opportunities to work with modern cloud and data technologies
How to Apply
Please submit your resume and LinkedIn profile, along with a brief description of one or more significant data-engineering projects you have worked on.
Candidates may be asked to complete a technical assessment covering SQL, Python, data modeling, ETL/ELT concepts, and data-pipeline design.