Senior Data Engineer

About the role

As a Senior Data Engineer, you will design, build, and optimize the data platform, including pipelines, models, and infrastructure that power analytics, reporting, and data-driven decision making across the QGenda product lines. You will serve as a technical leader with the team, contributing to architectural direction, driving best practices, and supporting complex data initiatives.

Responsibilities

• Deliver High-Quality, Scalable Data Engineering Solutions
• Arcitect, develop, test, and maintain ELT/ETL pipelines and data workflows supporting high-volume analytics
• Implement advanced data processing solutions and observability techniques to ensure data is accurate, fresh, and reliable
• Design and refine data models and semantic layers that support analytical self-service and advanced reporting
• Build data visualizations and dashboards supporting analytics use cases
• Strengthen Data Engineering Practices and Technical Standards
• Translate complex business and analytics requirements into efficient, scalable data solutions
• Apply best practices for version control, documentation, CI/CD, Infrastructure as Code, and data governance
• Contribute to code reviews, identify opportunities for architectural improvement, and contribute to continuous improvement efforts
• Collaborate Across Teams
• Partner with data engineers, DBAs, managers, and business stakeholders to deliver high-impact data products
• Provide technical guidance, informal mentorship, and support to other engineers in order to elevate team capabilities
• Communicate technical decisions, risks, and recommendations to both technical and non-technical audiences
• Drive Technical Excellence
• Optimize data pipelines and warehouse performance for speed, cost, and scalability
• Evaluate, prototype, and influence adoption of new tools, frameworks, and architectural patterns that enhance the data platform
• Contribute to data observability, incident response, and root-cause analysis for complex data issues
• Design and deliver AI-ready data products, ensuring data structures, metadata, and pipelines are suitable for natural language processing, predictive analytics, and other AI-driven capabilities

Requirements

• Exceptional analytical, problem solving, and debugging skills
• Strong communication with the ability to simplify and articulate technical concepts
• Ability to work collaboratively, influence architecture, and take ownership of deliverables
• Commitment to quality, reliability, and continuous improvement
• Experience in data engineering/analytics engineering, or related field
• Bachelor’s degree specializing in computing, data engineering, or related discipline
• Expertise in distributed data processing, data modeling, and performance tuning
• Strong proficiency in SQL and Python
• Experience with modern data stack components, such as: Cloud: AWS, GCP, Azure Warehouses: Snowflake, Redshift, BigQuery, etc. Orchestration: Airflow, MWAA, Composer, etc. Transformation: dbt, etc. Observability: data lineage/monitoring tools BI: Looker, Tableau, Power BI, etc. DevOps: Git, CI/CD, Terraform/CloudFormation
• Experience preparing datasets and data structures for AI/ML use cases, including NLP-driven analytics
• Experience with Glue, Dataflow

Qualifications

• 5-7+ years in data engineering/analytics engineering, or related field
• Bachelor’s degree specializing in computing, data engineering, or related discipline
• Expertise in distributed data processing, data modeling, and performance tuning
• Strong proficiency in SQL and Python
• Experience with modern data stack components, such as: Cloud: AWS, GCP, Azure Warehouses: Snowflake, Redshift, BigQuery, etc. Orchestration: Airflow, MWAA, Composer, etc. Transformation: dbt, etc. Observability: data lineage/monitoring tools BI: Looker, Tableau, Power BI, etc. DevOps: Git, CI/CD, Terraform/CloudFormation
• Experience preparing datasets and data structures for AI/ML use cases, including NLP-driven analytics
• Experience with Glue, Dataflow

Skills

• Exceptional analytical, problem solving, and debugging skills
• Strong communication with the ability to simplify and articulate technical concepts
• Ability to work collaboratively, influence architecture, and take ownership of deliverables
• Commitment to quality, reliability, and continuous improvement
• Experience in data engineering/analytics engineering, or related field
• Bachelor’s degree specializing in computing, data engineering, or related discipline
• Expertise in distributed data processing, data modeling, and performance tuning
• Strong proficiency in SQL and Python
• Experience with modern data stack components, such as: Cloud: AWS, GCP, Azure Warehouses: Snowflake, Redshift, BigQuery, etc. Orchestration: Airflow, MWAA, Composer, etc. Transformation: dbt, etc. Observability: data lineage/monitoring tools BI: Looker, Tableau, Power BI, etc. DevOps: Git, CI/CD, Terraform/CloudFormation
• Experience preparing datasets and data structures for AI/ML use cases, including NLP-driven analytics
• Experience with Glue, Dataflow

Benefits

• Fully company-paid options for medical (both in-person and virtual), dental and vision insurance
• Generous paid time off (PTO) policy
• Paid parental leave for birth, adoption or permanent placement
• 401(k) with company match
• Options to work in a hybrid-working model or remotely from home, depending on the position
• Annual Costco membership, cell phone stipend, commuter benefits, in-office perks and more

Pay

Competitive salary based on experience and qualifications

Schedule

Full-time, remote or hybrid working model