Senior Data Engineer

Compunnel Inc.; Irving (TX); On-site; USD 120,000 - 180,000; Full time

Job summary

Compunnel Inc. in Irving, TX, seeks a Senior Data Engineer to design and implement end‑to‑end data solutions for business needs.

You will lead architecture initiatives, partner with DevOps and product teams, and ensure scalable, reliable data platforms that support ad‑hoc requests and production deployments. You will mentor a small team of 2–5 engineers, optimize pipelines for performance and cost, and stay current with Spark, Databricks, Airflow, and cloud services (AWS/Azure).

Qualifications

• Bachelor's degree or equivalent in a quantitative discipline.
• 6+ years in data engineering with at least 2 years in data modeling.
• Proficiency with Python, SQL and cloud platforms (AWS/Azure).
• Strong knowledge of data architecture, ETL, data lakes, warehousing, and batch/streaming tech.
• Experience with Airflow, Spark/Databricks, and visualization tools.

Responsibilities

• Lead the design, architecture, and implementation of data initiatives with scalable platform capabilities.
• Optimize existing data workflows for performance and cost-efficiency.
• Mentor and lead a team of 2–5 data engineers.
• Collaborate with product managers, analysts, and architects to define requirements.
• Communicate technical concepts to non-technical stakeholders.
• Stay current with emerging data engineering technologies and recommend tools.
• Contribute to data engineering standards, processes, and documentation for consistency.

Skills

Python

SQL

Cloud (AWS/Azure)

Data modeling

Airflow

Spark

Databricks

Tableau

Power BI

Communication

Education

Bachelor's degree in Computer Science or related field

Tools

Databricks

Tableau

Power BI

Job description

The Senior Data Engineer is responsible for implementing end-to-end data solutions for ad-hoc requests, including requirement gathering, gap analysis of existing data platforms, and deploying solutions into production environments.

This role involves close coordination with DevOps teams and functional leads to ensure adherence to established standards and best practices.

Key Responsibilities

• Lead the design, architecture, and implementation of key data initiatives and platform capabilities, ensuring scalability and alignment with business needs.
• Optimize existing data workflows and systems to improve performance and cost-efficiency, while identifying innovative solutions.
• Mentor and lead a team of 2–5 data engineers, providing guidance on technical best practices and career development.
• Collaborate on cross-functional projects with product managers, analysts, and solutions architects to define requirements and deliver high-quality solutions.
• Communicate technical concepts clearly to non-technical stakeholders and translate business needs into actionable technical specifications.
• Stay current with emerging data engineering technologies and recommend tools and approaches to improve organizational capabilities.
• Contribute to the development of data engineering standards, processes, and documentation for consistency and maintainability.

Required Qualifications

• Education: Bachelor’s degree in Computer Science, Mathematics, Software Engineering, Management Information Systems, Computer/Electrical Engineering, or a related quantitative discipline.
• Experience: Minimum 6 years of relevant work experience in data engineering, including at least 2 years in data modeling.
• Strong technical foundation in Python, SQL, and experience with cloud platforms (e.g., AWS, Azure).
• Deep understanding of data engineering fundamentals, including database architecture, ETL processes, data lakes, data warehousing, and batch/streaming technologies.
• Experience with data orchestration tools (e.g., Airflow), data processing frameworks (e.g., Spark, Databricks), and visualization tools (e.g., Tableau, Power BI).
• Proven ability to lead engineering teams and foster a collaborative environment.
• Excellent communication and interpersonal skills for engaging both technical and non-technical audiences.