Full-time
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Job description
Key Responsibilities
- Design, develop, and implement new ETL (Extract, Transform, Load) jobs using Python and/or Spark to support various data initiatives.
- Migrate existing ETL processes from SSIS and SQL Server to modern data platforms, ensuring data integrity and performance.
- Maintain and optimize existing data pipelines and ETL processes for efficiency, reliability, and scalability.
- Develop and implement robust testing strategies for all ETL jobs to ensure data quality and accuracy.
- Collaborate with data architects, data scientists, and business analysts to understand data requirements and translate them into technical specifications.
- Participate actively in an agile development environment, including stand-ups, sprint planning, and retrospectives.
- Communicate effectively with users, stakeholders, and team members to gather requirements, provide updates, and resolve issues.
- Troubleshoot and resolve data-related issues and performance bottlenecks in a timely manner.
- Strong proficiency in Python and/or Apache Spark for data processing and ETL development.
- Strong SQL knowledge, with proven experience in writing complex queries, stored procedures, and optimizing database performance.
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Frequently asked questions
Q: What skills or qualities help someone succeed as a Data Software Engineer?
A: To succeed as a Data Software Engineer, key technical skills include proficiency in programming languages such as Python, Java, or C++, as well as expertise in data structures, algorithms, and software development methodologies like Agile. Additionally, strong soft skills like effective communication, problem-solving, and collaboration are crucial, as Data Software Engineers often work with cross-functional teams and stakeholders to design, develop, and deploy data-driven solutions. By combining technical expertise with strong soft skills, Data Software Engineers can effectively drive business outcomes, innovate, and adapt to the rapidly evolving landscape of data technology.
Q: What is the career path for a Data Software Engineer?
A: A Data Software Engineer's typical career progression involves starting as a Junior Software Engineer, where they focus on developing and maintaining data-driven software applications, and gradually advancing to roles such as Senior Software Engineer, Technical Lead, or Data Architect, where they oversee large-scale data systems and lead cross-functional teams. Key opportunities for skill development include learning programming languages like Python, SQL, and Java, as well as data science tools like Hadoop, Spark, and machine learning frameworks like TensorFlow and PyTorch. Long-term, Data Software Engineers may pursue leadership roles, such as Director of Engineering or Chief Technology Officer, or transition into related fields like data science, product management, or entrepreneurship.