$124.40K - $149.40K/yr
Other
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Job description
Basic Qualifications:
- A minimum of three years developing and deploying large scale advanced analytics applications into a production environment
- Minimum of five years, hands-on experience collaboratively developing code in object-oriented language for a production environment, including:
- Experience working in Linux environment (bash scripting)
- SQL (Data Normalization)
- Python
- Data Structures (arrays, lists, stacks, queues, trees, dictionaries, etc.)
- Experience in data mining techniques and methodologies (data prep/modeling, classification, regression, clustering, causal modeling, AI, machine learning, ensemble approaches)
- Advanced experience in data visualization tools with a strong grasp of effective data modeling and visualization practices
- Experience rapidly developing proof of concepts and testing new ideas, as well as scale these ideas into production ready models
- Experience leveraging cloud platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP) or Microsoft Azure
Preferred Qualifications:
- Advanced, relevant education, advanced Degree (Masters or PhD) in engineering, mathematics, physics, economics, computer science, statistics, or business analytics
- Experience with AWS big data services (Kinesis, Glue, EMR, Spectrum etc.)
- Experience with ETL & analytics tools (AWS, Matillion, Tallend, AtScale, etc.)
- Experience with data virtualization technologies (Denodo, etc.)
- Experience with data visualization tools (PowerBI, Tableau, etc.)
- Experience with Scrum/Agile delivery processes and DevOps concepts (i.e. CI/CD)
- Experience with data storage technologies (Redshift, S3, Hadoop, Graph, etc.)
- Experience with cloud Platforms (AWS, Azure, Google)
- Experience with multiple languages and syntax (Python, SQL, Apache Spark, etc.)
- Experience with Container based orchestration of big data pipelines
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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.