Associate Solutions Architect
3 weeks ago
The ideal candidate has a passion for solving business problems with technology and can effectively communicate business and technical needs to stakeholders. Please send in your resumes to Key Responsibilities Requirement Analysis: Collaborate with stakeholders to understand business requirements and data sources, and define the architecture and design of data pipelines to meet these requirements. Architecture Design: Design scalable, reliable, and efficient data pipelines to ingest, process, transform, and store large volumes of data from diverse sources, ensuring data quality and consistency. Technology Selection: Evaluate using POCs and recommend appropriate technologies, frameworks, and tools for building and managing data pipelines, considering factors like performance, scalability, and cost-effectiveness. Data Processing: Develop and implement data processing logic, including data cleansing, transformation, and aggregation, using technologies like AWS Glue, Batch, Lambda. Data Storage: Design and implement data storage solutions, including data lakes, data warehouses, and NoSQL databases, to store and manage data for analysis and reporting. Data Integration: Integrate data pipelines with external systems, APIs, and services to automate data workflows and ensure seamless data exchange between systems. Monitoring and Logging: Implement monitoring and logging solutions for data pipelines using tools like AWS CloudWatch to ensure data pipeline health and performance. Security and Compliance: Ensure data pipeline 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 pipeline 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 pipelines into their workflows. Continuous Improvement: Stay updated with the latest trends, tools, and technologies in data engineering and data pipeline architecture, and continuously improve data pipeline processes and methodologies. Desired Skills & Requirement Data Engineering: Understanding of data engineering concepts and principles, including data ingestion, processing, transformation, and storage, using tools and technologies such as AWS Glue, Batch, Lambda. Data Modeling: Knowledge of data modeling techniques and best practices for designing efficient and scalable data pipelines, including dimensional modeling, star schemas, and snowflake schemas. Programming Languages: Proficiency in programming languages commonly used in data engineering, such as Python, Java, or Scala, as well as experience with SQL for data querying and manipulation. 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. Data Governance: Awareness of data governance principles and practices, including data quality, data lineage, and data privacy, and ability to ensure compliance with relevant regulations and standards. Problem-solving Skills: Excellent problem-solving skills, with the ability to analyze complex data pipeline requirements and design solutions that meet business needs. 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 and data pipeline architecture. Academic Qualification and Industry Experience 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. At least 7 years of experience in solutions architecture, software development, or any other related field.
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