Data Engineer

LandEarly; United States; Remote; USD 110,000 - 170,000; Full time

Job summary

LandEarly is seeking a Data Engineer to design and maintain data infrastructure powering our job-matching and application platform. You will work on pipelines ingesting and processing large data volumes to support real-time matching and personalization.

You will collaborate with backend engineers and data scientists to expose clean datasets, ensuring data quality and scalability across batch and streaming workloads, with a focus on cost-efficient, observable systems.

Qualifications

• 3+ years of experience in data engineering or a related field.
• Strong proficiency in SQL and at least one programming language commonly used for data engineering (Python, Scala or Java).
• Experience building and orchestrating data pipelines with Airflow, dbt or similar.
• Hands-on experience with cloud data warehouses (Snowflake, BigQuery, Redshift) and cloud platforms (AWS, GCP or Azure).
• Solid understanding of data modeling, schema design and data quality best practices.
• Comfort working with both batch and streaming data processing.

Responsibilities

• Design, build, and maintain scalable data pipelines to ingest and process large volumes of job posting and user data.
• Develop and optimize ETL/ELT processes for structured and unstructured data sources.
• Build and maintain data models, warehouses, and schemas that support analytics and product features.
• Ensure data quality, reliability, and observability across pipelines through monitoring and testing.
• Collaborate with backend engineers and data scientists to expose clean, well-documented datasets for matching and personalization features.
• Optimize data infrastructure for performance and cost as data volume grows.

Skills

SQL

Python

Scala

Java

Batch processing

Streaming data

Data modeling

Observability

Collaboration

Performance tuning

Tools

Airflow

dbt

Snowflake

BigQuery

Redshift

AWS

GCP

Azure

Job description

LandEarly is looking for a Data Engineer to help build and maintain the data infrastructure that powers our job-matching and application platform. You'll work on the pipelines and systems that ingest, process, and serve data at scale, supporting features like real-time job matching, application tracking, and personalization.

What you'll do

• Design, build, and maintain scalable data pipelines to ingest and process large volumes of job posting and user data
• Develop and optimize ETL/ELT processes for structured and unstructured data sources
• Build and maintain data models, warehouses, and schemas that support analytics and product features
• Ensure data quality, reliability, and observability across pipelines through monitoring and testing
• Collaborate with backend engineers and data scientists to expose clean, well-documented datasets for matching and personalization features
• Optimize data infrastructure for performance and cost as data volume grows

What we're looking for

• 3+ years of experience in data engineering or a related field
• Strong proficiency in SQL and at least one programming language commonly used for data engineering (e.g., Python, Scala, or Java)
• Experience building and orchestrating data pipelines using tools such as Airflow, dbt, or similar
• Hands-on experience with cloud data warehouses (e.g., Snowflake, BigQuery, Redshift) and cloud platforms (AWS, GCP, or Azure)
• Solid understanding of data modeling, schema design, and best practices for data quality
• Comfort working with both batch and streaming data processing

Nice to have

• Experience working with high-volume, real-time data systems
• Familiarity with event-driven architectures and message queues (e.g., Kafka)
• Exposure to machine learning pipelines or feeding data into ML models