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# Data Engineer - Python/Snowflake

- **Company:** Robert Half
- **Location:** Houston, TX, United States
- **Experience:** Experienced
- **Contract:** Temporary contract
- **Skills:** Sql Data Warehouse, Legacy Database, Airflow, Amazon Web Services, Cloud Engineering, Code Review, Continuous Integration, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Transformation, Data Systems, Python (Programming Language), Operational Databases, Oracle (Applications), Performance Tuning, Query Optimization, Redis, Cloud Services, Software Engineering, Enterprise Data Management, Software Organization, Data Processing, Sql Optimization, Snowflake, Fastapi, Pandas, Real Time Data, Apache Kafka, Data Management, Terraform, Software Version Control, Data Pipelines, Docker, Databricks
- **Published:** October 8, 2026
- **Apply:** https://dejobs.org/x/x/1FB3492720DB4FE98FB90535A8E7520E/job/

## About the Role

* 3+ years of hands-on experience as a Data Engineer building and supporting production data pipelines.
 * Strong development experience with Python, including Pandas and related data-processing frameworks.
 * Advanced SQL skills with experience in query optimization, data modeling, and performance tuning.
 * Experience designing and supporting solutions within Snowflake or a comparable cloud data warehouse.
 * Hands-on experience with AWS services supporting data platforms and analytics environments.
 * Background building and maintaining ETL/ELT processes in a cloud ecosystem.
 * Experience with workflow orchestration tools such as:
 * Prefect
 * Airflow
 * Dagster
 * Strong understanding of data engineering fundamentals, software development best practices, version control, testing, and CI/CD.
 * Experience implementing monitoring, alerting, and operational support processes for production systems.
 * Excellent communication skills with the ability to partner directly with business stakeholders., * Experience with dbt for data transformation and modeling.
 * Exposure to Kafka, Redis, Oracle, Databricks, or similar enterprise data technologies.
 * Experience with Docker, Terraform, FastAPI, or cloud-native application development.
 * Background supporting enterprise analytics, market data, risk management, trading, supply chain, or operational reporting platforms.
Technology Doesn't Change the World, People Do.®

## Description

Join a growing data engineering team supporting mission-critical analytics and business operations within the energy, commodities trading, oil & gas, or financial services sector. This role is focused on designing and supporting cloud-based data solutions using Python, Snowflake, and AWS, enabling scalable, reliable, and high-performance data platforms.

You will work closely with business stakeholders, analysts, and engineering teams to build production-grade data pipelines, modernize legacy data environments, and deliver trusted datasets that support operational and strategic decision-making.

Responsibilities

 * Design, develop, and maintain scalable Python-based data pipelines that ingest, transform, and deliver data from internal and external sources.
 * Build and optimize data models within Snowflake, ensuring performance, scalability, and cost efficiency.
 * Develop ELT/ETL solutions leveraging cloud-native AWS services.
 * Create, monitor, and support batch and near real-time data integration workflows.
 * Work with structured, semi-structured, and time-series datasets from operational, trading, financial, and market data systems.
 * Modernize legacy data processes and migrate legacy database workloads into modern cloud architectures.
 * Implement data quality, reconciliation, monitoring, and alerting capabilities across the data platform.
 * Collaborate with analysts, traders, business users, and technology teams to deliver data solutions aligned with business objectives.
 * Participate in code reviews, testing, documentation, and deployment activities following engineering best practices.
 * Support production environments and troubleshoot data pipeline issues as needed.