Associate Solutions Architect

Department: Product

Location: CFRA India

• Requirement Analysis: Collaborate with stakeholders to understand business requirements and data sources and define the architecture and design of data engineering models to meet these requirements.


• Architecture Design: Design scalable, reliable, and efficient data engineering models, including algorithms, data pipelines, and data processing systems, to support business requirements and quantitative analysis.


• Technology Selection: Evaluate using POCs and recommend appropriate technologies, frameworks, and tools for building and managing data engineering models, considering factors like performance, scalability, and cost-effectiveness.


• Data Processing: Develop and implement data processing logic, including data cleansing, transformation, and aggregation, using technologies such as AWS Glue, Batch, Lambda.


• Quantitative Analysis: Collaborate with data scientists and analysts to develop algorithms and models for quantitative analysis, using techniques such as regression analysis, clustering, and predictive modeling.


• Model Evaluation: Evaluate the performance of data engineering models using metrics and validation techniques and iterate on models to improve their accuracy and effectiveness.


• Data Visualization: Create visualizations of data and model outputs to communicate insights and findings to stakeholders.


• Monitoring and Logging: Implement monitoring and logging solutions for data engineering models using tools like AWS CloudWatch to ensure model health and
  performance.


• Security and Compliance: Ensure data engineering model architecture complies with security best practices and regulatory requirements, implementing encryption, access controls, and data masking as needed.


• Documentation: Create and maintain documentation for data engineering model architecture, design, and implementation, including diagrams, data flow descriptions, and operational procedures.


• Collaboration: Collaborate with cross-functional teams, including data engineers, data scientists, and business analysts, to understand their requirements and integrate data engineering models into their workflows.


• Continuous Improvement: Stay updated with the latest trends, tools, and technologies in data engineering and quantitative analysis, and continuously improve data engineering model processes and methodologies.

• A minimum of 10+ years of development experience on enterprise applications


• Data Engineering: Understanding of data engineering principles and practices, including data ingestion, processing, transformation, and storage, using tools and technologies such as AWS Glue, Batch, Lambda.


• Quantitative Analysis: 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.


• Programming Languages: Proficiency in programming languages commonly used in data engineering and quantitative analysis, such as Python, R, Java, or Scala, as well as experience with SQL for data querying and manipulation.


• Big Data Technologies: 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.


• Cloud Computing: Proficiency in using AWS cloud services for data pipeline architecture and implementation.


• Data Integration: Understanding of data integration techniques and tools for integrating data from various sources, including batch and real-time data integration, and experience with ETL (Extract, Transform, Load) processes.


• Database Systems: Knowledge of database systems, including relational databases (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., MongoDB, Cassandra), and experience in designing and managing database schemas.


• Problem-solving Skills: Excellent problem-solving skills, with the ability to analyze complex data engineering and quantitative analysis problems, identify solutions, and
  implement them effectively.


• Communication and Collaboration: Strong communication and collaboration skills, with the ability to effectively communicate technical concepts to non-technical stakeholders and work effectively in a team environment.


• Learning Agility: A commitment to continuous learning and staying updated with the latest trends, tools, and technologies in data engineering, quantitative analysis, and machine learning.


• Bachelor's Degree: A bachelor's degree in Computer Science, Software Engineering, or a related field is often preferred, although equivalent experience and certifications can also be valuable. Experience with Financial domain knowledge is a plus.

• 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