Data Engineer

BAE Systems•Fort Walton Beach, FL

9d•$79,370 - $134,929•Remote

About The Position

BAE Systems is seeking an experienced Data Engineer to design, build, and maintain large-scale data warehouses, ETL pipelines, and AI/ML models that support data-driven decision-making. The role involves building and optimizing data systems, ensuring data quality and governance, integrating AI/ML models, and supporting customers and AWS engineers with Amazon Redshift and related data solutions. The engineer works with data scientists, analysts, customers, and business stakeholders to understand requirements and deliver reliable data products and services. The position requires a bachelor's or master's degree in Computer Science or a related field, plus at least six years of IT experience or an equivalent combination of education and work experience. Candidates should have substantial hands-on experience with data engineering, AWS Redshift, AWS Glue, Python/Spark ETL pipelines, SQL, and large-scale data systems. The position is remote and full-time, with a listed salary range of $79,370 to $134,929. BAE Systems describes its culture as performance-driven and values-led, emphasizing curiosity, creativity, and collaboration. Its mission includes developing technologies to support national security and benefit communities and the planet.

Requirements

Bachelor's or master's degree in Computer Science or a related field, with at least six years of experience in Information Technology, or an equivalent combination of education and work experience.
Six or more years of data engineering experience focused on designing and implementing large-scale data systems.
Five or more years of hands-on experience writing complex, highly optimized queries across large data sets using AWS Redshift, Oracle, and SQL Server.
Five or more years of hands-on experience using AWS Glue and Python/Spark to build production ETL pipelines, including writing test cases.
Proficiency in one or more programming languages, such as Python, Java, or Scala.
Strong understanding of database design principles, data modeling, and data governance.
Proficiency in SQL, including query optimization, indexing, and performance tuning.
Experience with data warehousing concepts, including star and snowflake schemas.
Experience with relational databases such as Oracle and SQL Server, NoSQL databases, and cloud-based data warehouses such as Redshift.
Experience with data processing frameworks such as Apache Kafka and Fivetran.
Experience building ETL pipelines using AWS Glue, Apache Airflow, Python, and PySpark.
Understanding of data quality and governance principles and best practices.
Experience with AWS services and best practices.
Excellent communication and collaboration skills, including the ability to work effectively with cross-functional teams.
Strong problem-solving and analytical skills, including the ability to break down complex problems into manageable components.
Ability to work in a fast-paced environment and adapt to changing priorities.
Strong attention to detail and ability to maintain high-quality documentation.
Commitment to continuous learning and professional development.

Nice To Haves

Experience with Dataiku.
Experience building reports using Power BI and Tableau.
Familiarity with agile development methodologies and version control systems such as Git.
Certification in data warehousing, ETL, or AI/ML, such as Certified Data Warehouse Architect or Certified ETL Developer.
Experience with agile development methodologies such as Scrum or Kanban.

Responsibilities

Design and implement data pipelines to ingest, extract, transform, and load data from various sources and store large data sets.
Build and maintain data warehouses, including data modeling, data governance, and data quality.
Implement data validation, cleansing, and governance policies to help ensure data quality, integrity, and security.
Optimize data systems for performance, scalability, and reliability.
Collaborate with customers to understand technical requirements and provide guidance on Amazon Redshift best practices.
Work with data scientists, analysts, and business stakeholders to understand data requirements and deliver data solutions.
Provide technical support for Amazon Redshift, including troubleshooting, performance optimization, and data modeling.
Integrate AI/ML models with ETL pipelines and data warehouses to enable predictive analytics and machine learning.
Develop and deploy AI/ML models using frameworks such as TensorFlow, PyTorch, or Scikit-learn.
Work with data scientists to develop and deploy data-driven products and features.
Identify and resolve data-related issues, including pipeline failures, data quality issues, and performance bottlenecks.
Develop technical documentation and knowledge base articles to help customers and AWS engineers troubleshoot common issues.