Engineering · Remote

Data Engineer

The Role

As a Data Engineer at Quaeris, you’ll build and optimize the data infrastructure that powers AI-driven analytics. You’ll work closely with engineering, product, and customer teams to design scalable data pipelines, model enterprise data, and ensure reliable, high-quality access to information across diverse cloud platforms.

This role is ideal for someone who enjoys solving complex data challenges, building robust ETL/ELT processes, and enabling AI applications through well-structured, governed data.

What You’ll Do

  • Design, develop, and maintain scalable ETL/ELT pipelines for structured and semi-structured data.
  • Build and optimize data models that support analytics, reporting, and AI applications.
  • Integrate data from enterprise systems, APIs, SaaS platforms, and cloud data warehouses.
  • Optimize SQL queries and data processing workflows for performance and reliability.
  • Implement data quality checks, validation frameworks, and monitoring to ensure data integrity.
  • Collaborate with product and engineering teams to support new features and customer deployments.
  • Develop reusable data ingestion frameworks and automation tools.
  • Work with semantic models and metadata to improve data discoverability and governance.
  • Ensure data security, compliance, and access controls align with enterprise standards.
  • Troubleshoot production data issues and continuously improve platform reliability.

What We’re Looking For

Required Qualifications

  • 3+ years of experience in Data Engineering or a related software engineering role.
  • Strong proficiency in SQL and Python.
  • Experience building ETL/ELT pipelines using modern data engineering practices.
  • Hands-on experience with cloud data warehouses such as Snowflake, Databricks, BigQuery, Amazon Redshift, or Microsoft Synapse.
  • Strong understanding of data modeling, schema design, and database optimization.
  • Experience integrating REST APIs and third-party data sources.
  • Familiarity with Git and CI/CD workflows.
  • Excellent problem-solving and debugging skills.
  • Strong communication and collaboration skills.

Preferred Qualifications

  • Experience with orchestration tools such as Apache Airflow, Dagster, or Prefect.
  • Knowledge of dbt for data transformation and modeling.
  • Experience with streaming technologies such as Apache Kafka or Azure Event Hubs.
  • Familiarity with Spark, PySpark, or distributed data processing frameworks.
  • Experience working with Azure, AWS, or GCP data services.
  • Understanding of data governance, metadata management, and security best practices.
  • Exposure to AI/ML pipelines, feature engineering, or Retrieval-Augmented Generation (RAG) architectures.

What Success Looks Like

Within your first year, you will:

  • Build reliable, scalable data pipelines that support enterprise analytics and AI workloads.
  • Improve data quality, observability, and pipeline performance across the platform.
  • Enable seamless integration with customer data ecosystems.
  • Contribute reusable frameworks that accelerate customer onboarding and deployment.
  • Partner with engineering and product teams to deliver new data capabilities that drive customer value.

Why Join Quaeris?

  • Help build the data foundation for enterprise AI and analytics.
  • Work with modern cloud data platforms and large-scale datasets.
  • Collaborate with a talented team focused on innovation, reliability, and customer success.
  • Influence the architecture of an AI-native analytics platform from the ground up.
  • Solve challenging problems at the intersection of data engineering, AI, and enterprise software.

Technologies You’ll Work With

  • Python
  • SQL
  • dbt
  • Apache Airflow / Dagster / Prefect
  • Snowflake, Databricks, BigQuery, Amazon Redshift, Microsoft Synapse
  • Spark / PySpark
  • Azure, AWS, GCP
  • REST APIs
  • Git & CI/CD
  • Docker & Kubernetes

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