Data Engineer

Job Type: Full time
Location: Remote

About the Company
Our client is a venture backed AI startup building an enterprise reasoning platform that helps large organizations make and execute growth decisions. Their product combines advanced AI reasoning, data activation, and agentic workflows to turn strategy into action and deliver measurable business outcomes.

About the Role
We are searching for an ambitious go getter who welcomes the challenge of meeting the needs of a hyper growth startup. As a Data Engineer, you will contribute to the company's data infrastructure, playing a key role in developing and maintaining the systems that power its AI platform. Your work will directly influence product features, client outcomes, and strategic business decisions.

You will collaborate with Forward Deployed Engineers, Data Scientists, Backend Engineers, Client IT, and business stakeholders to build and support scalable pipelines that serve billions of rows of structured and unstructured data weekly, enabling high impact insights across multiple industries.

Responsibilities

Technical Development

• Implement and support scalable, high performance data infrastructure to handle complex data processing and integration across platforms
• Extract and ingest data from a wide variety of structured and unstructured web sources
• Develop developer facing tools, abstractions, and frameworks that empower engineering teams to easily build and manage data pipelines
• Leverage modern cloud platforms (e.g., Snowflake, Databricks) to execute ETL jobs, share data securely, and enforce granular access controls
• Work with AI driven autonomous agents to streamline data operations and enable advanced data exploration on client datasets
• Engineer and manage resilient ETL/ELT pipelines using tools like Airflow, Snowpark, Databricks, Cloud Run, and Apache Beam
• Define, deploy, and maintain data infrastructure using Infrastructure as Code (IaC) principles

Collaboration

• Partner with Forward Deployed Engineers and Data Scientists to translate business and machine learning requirements into production ready data workflows
• Ensure consistent, timely delivery of clean, high quality data to drive model development and core product features

Monitoring and Reliability

• Create and manage real time monitoring systems for data ingestion and transformation pipelines
• Automate security scanning and compliance checks within automated pipeline workflows
• Proactively identify and resolve issues to maintain high levels of system reliability and data integrity

Minimum Qualifications

• 2 to 5 years of experience in Data Engineering or Data Infrastructure roles
• Bachelor's degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience)
• Proven experience building reliable data pipelines supported by automated unit and integration tests
• Deep understanding of ETL/ELT architecture, data modeling best practices, and distributed systems
• Hands on experience with agentic systems, ideally for data extraction and validation
• Background using distributed computing frameworks (e.g., Apache Beam, Spark)
• Demonstrated skill using AI driven tooling to automate workflows and accelerate software engineering
• Strong debugging, analytical, and technical problem solving capabilities
• Excellent communication skills with a track record of effective cross functional collaboration

Preferred Qualifications

• Experience delivering enterprise software solutions for Fortune 500 clients across Financial Services, Industrials, or Consumer Products
• Prior experience in high growth startup environments
• Experience working with data virtualization layers like Trino, Starburst, and Presto
• Interest in market dynamics, current industry events, and emerging technology trends
• Experience designing and creating custom Agent Skills and/or Model Context Protocol (MCP) integrations
• Proficiency in leveraging APIs and modern web scraping techniques for data ingestion
• Familiarity with Infrastructure as Code tooling such as Terraform
• Familiarity with graph databases (e.g., Neo4j) and graph query languages

Tech Stack

• Languages: Python
• Frameworks: Apache Beam, Spark, FastAPI, Airflow
• Cloud: Google Cloud Platform (GCP), AWS, Azure
• Data: Elasticsearch, Snowflake, Databricks, Neo4j, PostgreSQL, MongoDB, GCS
• Infrastructure and DevOps: Docker, Terraform, GitHub Actions, Cloud Run
• Frontend (collaboration context): Next.js

Perks

• Competitive compensation and equity in a rapidly growing company
• Unlimited vacation
• 100% company paid health, dental, and vision insurance