Data Engineering Manager

Must be local to TX

Job Summary:

We are seeking a Data Engineering Manager to oversee the design, development, and maintenance of our data infrastructure and pipelines. The ideal candidate will have a strong technical background in data engineering, excellent leadership skills, and a commitment to data security and compliance.

Key Responsibilities:

• Lead and mentor a team of data engineers, promoting a culture of collaboration and excellence.
• Design and implement robust data architectures and pipelines to support analytics and business intelligence, while ensuring data security protocols are followed.
• Collaborate with cross-functional teams to define data requirements and ensure data availability and integrity.
• Oversee data quality management, ensuring accuracy and reliability of data across systems.
• Establish and enforce data security policies and best practices to protect sensitive information.
• Drive the adoption of best practices in data engineering, including data modeling, ETL processes, and data governance.
• Manage project timelines, ensuring that data engineering initiatives align with business goals.
• Stay current with industry trends and emerging technologies to enhance our data capabilities.

Qualifications:

• Bachelor’s degree in computer science, Engineering, or a related field.
• More than 12 years of experience in software development, with atleast 5 years in a leadership role.
• Proficiency in data engineering tools and technologies (e.g. Microsoft SQL Server, Databricks, NoSQL-MongoDB or Dynamo DB).
• Strong understanding of ETL processes, data warehousing, and data modeling (e.g. Apache Kafka, Microsoft Azure Data Factory, Snowflake).
• Experience with cloud platforms (AWS or Azure) and data pipeline orchestration tools (e.g., Apache Airflow).
• Excellent problem-solving skills and attention to detail.
• Strong communication and interpersonal skills.

Preferred Qualifications:

• Familiarity with machine learning concepts, and tools.
• Experience in managing and optimizing large-scale data environments.
• Previous experience in an Agile/Scrum environment.