Senior Software Developer (Quantitative Solutions)
CFRA
IndiaremotePosted 1 month ago
Skill Required
Senior-Software-DeveloperQuantitative-AnalystData-EngineerFintech-Software-EngineerQuantitative-DeveloperSenior-Quantitative-DeveloperQuantitative-Software-EngineerQuantitative-Risk-Software-DeveloperSoftware EngineerSoftware DeveloperMachine Learningdata engineeringDesign PatternsKafkaGCPEngineeringR ProgrammingData StructuresPyTorchTableauTestNGPythonHadoopdesignScalaSparkFlinkAzureCloudJavaHiveAPIsSQLAWSGitFulltime
Key highlights
- Senior-level position
- Python on AWS cloud infrastructure
- Initial design and development of a new customer-facing application framework
- Competitive pay + Annual Performance Bonus
- 21 days Annual Vacation + 8 sick days + 6 casual days
- Medical, Accidental & Term Life Insurance; Telehealth, OPD
Role overview
CFRA is seeking a Senior Software Developer to lead the design and development of its next generation of quantitative solutions using a modern cloud-native technology stack with Python on AWS cloud infrastructure. This is a rare opportunity to make a significant impact on both the team and the organization by being part of the initial design and development of a new customer-facing application framework that will serve as the foundation for all future development at CFRA. The team uses a contemporary stack in the AWS cloud to design, build, and maintain robust data delivery pipelines via APIs and Feeds.
Responsibilities
- Lead the design and development of quantitative data engineering models, including algorithms, data pipelines, and data processing systems, to support business requirements
- Develop and maintain data processing pipelines to ingest, clean, transform, and aggregate large volumes of data from various sources, ensuring data quality and reliability
- Design and implement algorithms for data analysis, machine learning, and statistical modeling, using techniques such as regression analysis, clustering, and predictive modeling
- Identify and implement optimizations to improve the performance and efficiency of data processing and modeling algorithms, considering factors like scalability and resource utilization
- Create visualizations of data and model outputs to communicate insights and findings to stakeholders
- Implement data quality checks and validation processes to ensure the accuracy, completeness, and consistency of data used in models and analyses
- Evaluate the performance of data engineering models using metrics and validation techniques, and iterate on models to improve their accuracy and effectiveness
- Collaborate with data scientists, analysts, and business stakeholders to understand requirements, develop models, and deliver insights that drive business decisions
- Document the design, implementation, and evaluation of data engineering models, including assumptions, methodologies, and results, to ensure reproducibility and transparency
- Stay updated with the latest trends, tools, and technologies in quantitative data engineering and data science, and continuously improve your skills and knowledge
Requirements
- Strong background in data engineering principles, including data ingestion, data processing, data transformation, and data storage, using tools and frameworks such as Apache Spark, Apache Flink, or AWS Glue
- Proficiency in quantitative analysis techniques, including statistical modeling, machine learning, and data mining, with experience in implementing algorithms for regression analysis, clustering, classification, and predictive modeling
- Proficiency in programming languages commonly used for data engineering and quantitative analysis, such as Python, R, Java, or Scala, as well as experience with SQL for data querying and manipulation
- Familiarity with big data technologies and platforms, such as Hadoop, Apache Kafka, Apache Hive, or AWS EMR, for processing and analyzing large volumes of data
- Experience in data visualization techniques and tools, such as Matplotlib, Seaborn, or Tableau, for creating visualizations of data and model outputs to communicate insights effectively
- Familiarity with machine learning frameworks and libraries, such as PyTorch for implementing and deploying machine learning models
- Experience with cloud computing platforms, such as AWS, Azure, or Google Cloud Platform, and proficiency in using cloud services for data engineering and model deployment
- Strong software development skills, including proficiency in software design patterns, version control systems (e.g., Git), and software testing frameworks, to develop robust and maintainable code
- Excellent problem-solving skills, with the ability to analyze complex data engineering and quantitative analysis problems, identify solutions, and implement them effectively
- Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams, including data scientists, analysts, and business stakeholders, to understand requirements and deliver solutions
- Domain knowledge in areas such as finance, healthcare, or marketing, depending on the industry, to understand the context and requirements of data engineering models in specific domains
- A commitment to continuous learning and staying updated with the latest trends, tools, and technologies in data engineering, quantitative analysis, and machine learning
Nice to have
- Passion for solving business problems with technology
- Ability to effectively communicate business and technical needs to stakeholders
- Values collaboration with colleagues
- Desire to have an immediate, tangible impact
Benefits
- 21 days of Annual Vacation
- 8 sick days
- 6 casual days
- 1 paid Volunteer Day
- Medical, Accidental & Term Life Insurance
- Telehealth, OPD
- Competitive pay
- Annual Performance Bonus
Additional details
- Work will involve being part of the initial design and development of a new customer-facing application framework that will serve as the foundation for all future development at CFRA
- CFRA is a leading global independent financial insights and data company
- Originally posted on Himalayas