Data engineer Jobs in Grand Prairie, TX

Remote - Lead Data Engineer - up to $190K base - join a team building systems to make data driven business decisions

This Jobot Job is hosted by : Lucas Watson

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Salary : $150,000 - $190,000 per year

A bit about us :

Our client, in the financial services industry, is seeking a Lead Data Engineer to join their team. This is a full-time, direct hire, remote role that can pay $150-190K base salary plus benefits, depending on experience.

Why join us?

This role is ideal for someone who thrives in a dynamic, fast-paced environment, enjoys solving complex data problems, and is passionate about driving innovation in data engineering. If you're looking to make an impact on the financial landscape with cutting-edge data solutions this could be for you!

Job Details

Core Responsibilities :

• Lead the design and implementation of end-to-end data pipelines, from extraction (API, scraping, pyodbc) to cleansing / transformation (Python, TSQL) and loading into SQL databases or data lakes.
• Oversee the development of robust data architectures that support efficient querying and analytics, ensuring high-performance and scalable data workflows.
• Collaborate with data scientists, software developers, business intelligence teams, and stakeholders to develop and deploy data solutions that meet business needs.
• Ensure smooth coordination between engineering and other teams to translate business requirements into technical solutions.
• Guide the development of data models and business schemas, ensuring that they are optimized for both relational (3NF) and dimensional (Kimball) architectures.
• Lead the creation of scalable, reliable data models and optimize them for performance and usability.
• Develop and maintain the infrastructure for large-scale data solutions, leveraging cloud platforms (e.g., Azure) and containerization technologies (e.g., Docker).
• Lead the use of modern data platforms such as Snowflake and Fabric, ensuring their effective use in large-scale data solutions.
• Manage and optimize data pipelines using tools such as Apache Airflow, Prefect, DBT, and SSIS, ensuring that all stages of the pipeline (ETL) are efficient, scalable, and reliable.
• Ensure robust testing, monitoring, and validation of all data systems and pipelines.
• Drive continuous improvement in data engineering processes and practices, ensuring they remain cutting-edge, efficient, and aligned with industry best practices.
• Foster a culture of clean code, best practices, and rigorous testing across the team.
• Strong experience with data pipeline design and implementation, including data extraction, transformation, and loading (ETL) processes.
• Proficiency in SQL (Postgres, SQL Server) and experience with modern data warehouse solutions (e.g., Snowflake, Fabric).
• Expertise in Python for data engineering tasks, including data manipulation (Pandas, NumPy) and workflow management (Dask, PySpark, FastAPI).
• Solid knowledge of cloud platforms (Azure, AWS) and big data technologies (Hadoop, Spark).
• Hands-on experience with Docker, Kubernetes, and containerized environments.
• Strong understanding of dimensional modeling (Kimball), relational database design (3NF), and best practices in data architecture.
• Experience with API development, including building and managing API integrations.
• Proficiency with orchestration tools like Prefect or Airflow for workflow management.
• Strong focus on testing and validation, ensuring that all data systems meet reliability and performance standards.

Experience & Qualifications :

Interested in hearing more? Easy Apply now by clicking the "Apply Now" button.