Azure Data Engineer

Recru; Fort Worth, Arlington, Dallas (TX, TX, TX); On-site; USD 120,000 - 150,000; Full time

Job summary

Recru seeks a highly skilled Data Engineer to modernize and strengthen an enterprise data environment. You will design and build scalable data solutions across the data lifecycle, from requirements through deployment and production support.

The role is contract-to-hire in Fort Worth, TX, offering ownership of critical data pipelines and collaboration with business and technology stakeholders to drive analytics, automation, and AI initiatives.

Qualifications

• 5+ years hands-on data engineering experience with production data solutions.
• Strong Azure cloud data engineering background.
• Advanced SQL with Python and/or PySpark.
• Experience building ETL/ELT pipelines and analytics-ready datasets.
• Experience with Azure Data Factory, Databricks, ADLS, Snowflake, Fabric, Synapse, or similar.
• Knowledge of data modeling, data warehousing, and lakehouse architectures.
• Experience integrating data from APIs, databases, enterprise applications, and third-party platforms.
• Understanding of data quality, governance, security and access control.
• Experience with Git-based source control and CI/CD.
• Excellent communication and business acumen; able to work across teams.
• Ability to work independently and take ownership.

Responsibilities

• Design, build, and maintain scalable ETL/ELT pipelines and data integrations.
• Develop analytics-ready data models and datasets for reporting, analytics, and decision-making.
• Build and optimize data solutions in a Microsoft Azure cloud environment.
• Integrate data across enterprise applications, APIs, databases, cloud platforms, and third-party systems.
• Improve data quality, reliability, governance, lineage, and security across the data lifecycle.
• Partner with business stakeholders to translate needs into scalable technical solutions.
• Own data initiatives from requirements and solution design through development, testing, deployment, and ongoing support.
• Troubleshoot complex data and integration issues and drive resolution with minimal oversight.
• Apply engineering practices around source control, testing, CI/CD, documentation, and change management.
• Support modernization of the data ecosystem and growing analytics, automation, and AI capabilities.

Skills

Data engineering

Azure cloud

SQL

Python

PySpark

ETL/ELT pipelines

Data quality & governance

CI/CD

Stakeholder collaboration

Independent ownership

Tools

Azure Data Factory

Azure Databricks

ADLS

Snowflake

Microsoft Fabric

Synapse

dbt

Git/CI/CD tooling

Job description

We are seeking a highly skilled, self-directed Data Engineer to help modernize and strengthen an enterprise data environment. This individual will design and build scalable data solutions that improve how data is integrated, managed, and leveraged across the business.

The ideal candidate combines strong hands-on engineering skills with business acumen and an ownership mindset. This person should be comfortable working independently, partnering directly with business and technology stakeholders, and taking solutions from initial requirements through design, development, deployment, and production support.

This is a contract-to-hire opportunity with the potential to convert to a full-time position within approximately 6 to 12 months based on business needs and mutual fit.

Key Responsibilities

• Design, build, and maintain scalable ETL/ELT pipelines and data integrations across enterprise systems.
• Develop reliable, analytics-ready data models and datasets supporting reporting, analytics, and business decision-making.
• Build and optimize data solutions within a Microsoft Azure cloud environment.
• Integrate data across enterprise applications, APIs, databases, cloud platforms, and third-party systems.
• Improve data quality, reliability, monitoring, lineage, security, and governance across the data lifecycle.
• Partner directly with business stakeholders to understand needs and translate business problems into scalable technical solutions.
• Own data initiatives from requirements and solution design through development, testing, deployment, and ongoing support.
• Troubleshoot complex data and integration issues and drive them through resolution with minimal oversight.
• Apply strong engineering practices around source control, testing, CI/CD, documentation, and change management.
• Support the continued modernization of the data ecosystem and growing analytics, automation, and AI capabilities.

Qualifications

• 5+ years of hands-on data engineering experience building and supporting production data solutions.
• Strong experience with Microsoft Azure and modern cloud data engineering.
• Advanced SQL skills with hands-on experience in Python and/or PySpark.
• Strong experience developing ETL/ELT pipelines, data integrations, and analytics-ready datasets.
• Experience with modern data technologies such as Azure Data Factory, Azure Databricks, ADLS, Snowflake, Microsoft Fabric, Synapse, or similar platforms.
• Strong understanding of data modeling, data warehousing, and modern lakehouse architectures.
• Experience integrating data from APIs, databases, enterprise applications, and third-party platforms.
• Understanding of data quality, governance, security, lineage, and access-control principles.
• Experience with Git-based source control, CI/CD, and modern data engineering practices.
• Strong communication and business acumen with the ability to work effectively across technical and non-technical teams.
• Demonstrated ability to work independently, navigate ambiguity, and take ownership from problem identification through resolution.

Preferred Experience

• Databricks, Delta Lake, medallion/lakehouse architecture, dbt, or Microsoft Fabric.
• Snowflake development, integration, or optimization.
• Power BI or other enterprise analytics platforms.
• Experience enabling data for AI/ML, generative AI, or automation initiatives.
• Experience within consumer products, e-commerce, subscription-based, direct-selling, or other transaction-heavy environments.