$113.50K - $136.30K/yr
Other
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Job description
We are seeking a highly experienced Engineer to join our team and own the development of a tiered data architecture for our data. In this role, they would be responsible for crafting and building a comprehensive data architecture that will enable seamless data integration and enable the delivery of high-quality insights to our leadership and business stakeholders.
Skills Required: Apache Spark, SQL, git, Programming Language (Python, Java, Scala) Nice to have: Understanding of Design Patterns, Able to discuss tradeoffs between RDBMS vs Distributed Storage
Key Qualifications:
- Proven experience in data engineering, data architecture, or a related field
- Experience in building and deploying tiered data architecture for analytics data is a plus
- Strong understanding of data modeling, data warehousing, and ETL concepts
- Proficiency in SQL and experience with at least one major data analytics platform, such as Hadoop or Spark
- Experience with data orchestration tools like Airflow is a nice to have
- Excellent problem-solving and analytical skills, and the ability to work well under tight deadlines
- Excellent interpersonal skills and the ability to collaborate effectively with cross-functional teams
Description:
- Design and implement a tiered data architecture that integrates analytics data from multiple sources in an efficient and effective manner.
- Develop data models and mapping rules to transform raw data into actionable insights and reports.
- Collaborate with the analytics and business teams to understand their requirements and deliver solutions that meet their needs.
- Ensure data quality and accuracy by developing data validation and reconciliation processes.
- Play an active role in the development and maintenance of user documentation, including data models, mapping rules, and data dictionaries.
- Collaborate with multi-functional teams to define and implement data governance policies and standards.
- Stay informed about the latest developments in data analytics and data management technologies and recommend new tools and methodologies to improve the semantic layer.
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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.
