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Remote Python Data Engineer Jobs (NOW HIRING)

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Data Engineer

Brooklyn, NY · Remote

$130K - $200K/yr

We're a remote team but have a small office in Brooklyn, New York. We are looking for a data ... Skills should include Python, Data Warehouses (such as Clickhouse, Snowflake, or BigQuery) * Nice ...

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How much do remote python data engineer jobs pay per year?

As of Jun 18, 2026, the average yearly pay for remote python data engineer in the United States is $139,971.00, according to ZipRecruiter salary data. Most workers in this role earn between $110,500.00 and $164,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Remote Python Data Engineer, and why are they important?

To thrive as a Remote Python Data Engineer, you need strong proficiency in Python, data modeling, and ETL pipeline development, typically backed by a degree in computer science or a related field. Familiarity with tools like Apache Airflow, SQL databases, cloud platforms (such as AWS or GCP), and certifications in data engineering are highly valuable. Excellent problem-solving, communication, and self-motivation are crucial soft skills for remote collaboration and project delivery. These skills ensure efficient data processing, seamless teamwork across distributed environments, and the reliable delivery of scalable data solutions.

How do Remote Python Data Engineers typically collaborate with distributed teams to ensure smooth project delivery?

Remote Python Data Engineers work closely with cross-functional teams, including data scientists, analysts, and DevOps engineers, often using collaboration tools like Slack, Jira, and GitHub to coordinate work. Regular virtual meetings, clear documentation, and code reviews are essential for maintaining alignment and ensuring code quality. Emphasis is placed on asynchronous communication and well-structured version control practices to overcome time zone differences and keep projects on track. Adapting to these remote workflows is key for successful project delivery in a distributed environment.

What is a Remote Python Data Engineer?

A Remote Python Data Engineer is a professional who specializes in designing, building, and maintaining data pipelines and architectures, primarily using Python, while working from a location outside of a traditional office setting. They are responsible for collecting, transforming, and storing vast amounts of data to support analytics and business intelligence tasks. Their role often involves working with cloud platforms, databases, and big data technologies to ensure efficient data processing and accessibility for other teams. Remote Python Data Engineers collaborate with data scientists, analysts, and developers, leveraging Python's extensive libraries to automate workflows and solve complex data challenges.

What is the difference between Remote Python Data Engineer vs Remote Data Scientist?

AspectRemote Python Data EngineerRemote Data Scientist
Required CredentialsBachelor's in CS, Data Engineering certificationsBachelor's in CS, Statistics, Data Science certifications
Work EnvironmentData pipelines, ETL processes, cloud platformsData analysis, modeling, machine learning projects
Employer & Industry UsageTech companies, finance, healthcareResearch institutions, tech firms, marketing agencies
Common Search & ComparisonYesYes

Remote Python Data Engineers focus on building and maintaining data pipelines and infrastructure using Python, while Remote Data Scientists analyze data, develop models, and generate insights. Both roles often collaborate but serve different functions within data teams.

What cities are hiring for Remote Python Data Engineer jobs? Cities with the most Remote Python Data Engineer job openings:
What are the most commonly searched types of Python Data Engineer jobs? The most popular types of Python Data Engineer jobs are:
What states have the most Remote Python Data Engineer jobs? States with the most job openings for Remote Python Data Engineer jobs include:
Remote Python Data Engineer jobs near you

Python Data Scientist / Data Engineer

HRC Global Services

Virginia Beach, VA • Remote

Full-time

Posted 29 days ago

Job description

Python Data Scientist / Data Engineer

Python Data Scientist / Data Engineer (PySpark, ETL, Notebooks, Databricks) Summary We're hiring a versatile Python Data Scientist / Data Engineer to design, implement, and productionize scalable data pipelines and analytics workflows.

The role requires deep PySpark experience, strong ETL design, experience with interactive notebooks, and familiarity with Databricks or similar managed Spark platforms.

Key responsibilities

* Design and build robust ETL/ELT pipelines using PySpark for batch and near-real-time workflows.

* Develop, optimize, and schedule Spark jobs on Databricks (or equivalent Spark environment).

* Work with data scientists and product teams to transform business requirements into data models, features, and dashboards.

* Implement data validation, monitoring, and lineage to ensure data quality and reliability.

* Create reproducible notebooks for exploration, prototyping, and handoff to production pipelines.

* Tune performance of Spark jobs (memory, partitioning, shuffle reduction) and optimize cluster usage.

* Collaborate on building CI/CD for data workflows, including tests for data transformations and schema changes.

* Document architecture, ETL processes, and best practices.

Required qualifications

* 3+ years experience in data engineering, data science, or analytics engineering roles.

* Strong Python programming and hands-n PySpark experience developing data transformations at scale.

* Experience with Databricks (jobs, clusters, Delta Lake) or equivalent managed Spark platforms.

* Solid understanding of ETL/ELT patterns, data modeling, and data warehousing concepts.

* Comfortable using Jupyter or Databricks notebooks for exploration and prototyping.

* Experience with SQL and working knowledge of cloud data storage systems (S3, ADLS) and databases.

* Familiarity with orchestration tools (Airflow, Prefect, Dagster) and awareness of CI/CD for data pipelines.