Posted today · be early
Senior Software Developer (Quantitative Solutions)
CFRA
IndiaremotePosted today
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
- Competitive pay with an annual performance bonus
- Senior Software Developer level
- 21 days of annual vacation plus additional sick, casual, and volunteer days
- Work with a modern cloud‑native stack using Python on AWS
- Opportunity to design and develop a new customer‑facing application framework
- Medical, accidental and term life insurance and telehealth coverage
Role overview
The Senior Software Developer will be responsible for developing CFRA’s next generation of quantitative solutions using a modern cloud‑native technology stack with Python on AWS cloud infrastructure. This role offers a rare opportunity to impact both the team and the organization by contributing to the initial design and development of a new customer‑facing application framework that will serve as the foundation for all future development at CFRA.
Responsibilities
- Model Development: Lead the design and development of quantitative data engineering models, including algorithms, data pipelines, and data processing systems, to support business requirements.
- Data Processing: Develop and maintain data processing pipelines to ingest, clean, transform, and aggregate large volumes of data from various sources, ensuring data quality and reliability.
- Algorithm Development: Design and implement algorithms for data analysis, machine learning, and statistical modeling, using techniques such as regression analysis, clustering, and predictive modeling.
- Performance Optimization: Identify and implement optimizations to improve the performance and efficiency of data processing and modeling algorithms, considering factors like scalability and resource utilization.
- Data Visualization: Create visualizations of data and model outputs to communicate insights and findings to stakeholders.
- Data Quality Assurance: Implement data quality checks and validation processes to ensure the accuracy, completeness, and consistency of data used in models and analyses.
- Model Evaluation: Evaluate the performance of data engineering models using metrics and validation techniques, and iterate on models to improve their accuracy and effectiveness.
- Collaboration: Collaborate with data scientists, analysts, and business stakeholders to understand requirements, develop models, and deliver insights that drive business decisions.
- Documentation: Document the design, implementation, and evaluation of data engineering models, including assumptions, methodologies, and results, to ensure reproducibility and transparency.
- Continuous Learning: 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.
Nice to have
- Domain knowledge in areas such as finance, healthcare, or marketing to understand the context and requirements of data engineering models in specific domains.
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
- The ideal candidate has a passion for solving business problems with technology and can effectively communicate business and technical needs to stakeholders.
- We are looking for candidates that value collaboration with colleagues and having an immediate, tangible impact for a leading global independent financial insights and data company.
- The team uses a contemporary stack in the AWS cloud to design, build, and maintain robust data delivery pipelines via APIs and Feeds.
- Originally posted on Himalayas
