Role Proficiency:

Independently provides expertise on data analysis techniques using software tools; streamlining business processes and managing team

Outcomes:

      Managing and designing the reporting environment including data sources security and metadata.       Providing technical expertise on data storage structures data mining and data cleansing.       Supporting the data warehouse in identifying and revising reporting requirements.       Supporting initiatives for data integrity and normalization.       Assessing tests and implementing new or upgraded software and assisting with strategic decisions on new systems.       Synthesize both quantitative and qualitative data into insights       Generating reports from single or multiple systems.       Troubleshooting the reporting database environment and reports.       Understanding business requirements and translating it into executable steps for the team members.   Identify and recommend new ways to streamline business processes   Illustrates data graphically and translates complex findings into written text.   Locating results to help the clients make better decisions. Get feedback from clients and offer to build solutions based on the feedback.   Review the team’s deliverables before sending final reports to stakeholders.   Support cross-functional teams with data reports and insights on data.   Training end users on new reports and dashboards. Set FAST goals and provide feedback on FAST goals of reportees

Measures of Outcomes:

      Quality - number of review comments on codes written       Accountable for data consistency and data quality.       Number of medium to large custom application data models designed and implemented       Illustrates data graphically and translates complex findings into written text.       Number of results located to help clients make informed decisions.       Attention to detail and level of accuracy.       Number of business processes changed due to vital analysis.       Number of Business Intelligent Dashboards developed       Number of productivity standards defined for project   Manage team members and review the tasks submitted by team members   Number of mandatory trainings completed

Outputs Expected:

Determine Specific Data needs:

Work with departmental managers to outline the specific data needs for each business method analysis project

Management and Strategy:

Oversees the activities of analyst personnel and ensures the efficient execution of their duties.

Critical business insights:

Mines the business’s database in search of critical business insights and communicates findings to the relevant departments.

Code:

Creates efficient and reusable SQL code meant for the improvement
manipulation
and analysis of data. Creates efficient and reusable code. Follows coding best practices.

Create/Validate Data Models:

Builds statistical models; diagnoses
validates
and improves the performance of these models over time.

Predictive analytics:

Seeks to determine likely outcomes by detecting tendencies in descriptive and diagnostic analysis

Prescriptive analytics:

Attempts to identify what business action to take

Code Versioning:

Organize and manage the changes and revisions to code. Use a version control tool like git
bitbucket. etc.

Create Reports:

Create reports depicting the trends and behaviours from the analysed data

Document:

Create documentation for own work as well as perform peer review of documentation of others' work

Manage knowledge:

Consume and contribute to project related documents
share point
libraries and client universities

Status Reporting:

Report status of tasks assigned Comply to project related reporting standards/process

Skill Examples:

      Analytical Skills: Ability to work with large amounts of data: facts figures and number crunching.       Communication Skills: Communicate effectively with a diverse population at various organization levels with the right level of detail.       Critical Thinking: Data analysts must look at the numbers trends and data and come to new conclusions based on the findings.       Presentation Skills - reports and oral presentations to client Strong meeting facilitation skills as well as presentation skills. Attention to Detail: Making sure to be vigilant in the analysis to come to correct conclusions. Mathematical Skills to estimate numerical data. Work in a team environment Proactively ask for and offer help

Knowledge Examples:

Knowledge Examples

      Database languages such as SQL       Programming language such as R or Python       Analytical tools and languages such as SAS & Mahout.       Proficiency in MATLAB.       Data visualization software such as Tableau or Qlik or Power BI.       Proficient in mathematics and calculations.       Spreadsheet tools such as Microsoft Excel or Google Sheets       DBMS       Operating Systems and software platforms Knowledge about customer domain and also sub domain where problem is solved

Additional Comments:

As a Senior Backend Developer, you will be responsible for designing, developing, and maintaining server-side applications and backend services using Python. You will work closely with cross-functional teams to ensure seamless integration with cloud infrastructure (primarily AWS) and ensure that the applications are scalable, resilient, and optimized for performance. In this role, you'll leverage AWS services (including AWS Glue and other managed services) and focus on Infrastructure as Code (IaC) using AWS CDK to automate infrastructure provisioning. You will also play a key role in ensuring the security, availability, and reliability of the backend systems while mentoring junior engineers and helping to shape the future of our architecture. Key Responsibilities: • Backend Development: Build and maintain scalable, high-performance, and secure backend applications using Python. • AWS Architecture: Work with a variety of AWS services (including AWS Glue, S3, Lambda, EC2, RDS, and more) to design robust, scalable architectures. • Infrastructure as Code (IaC): Utilize AWS CDK (Cloud Development Kit) to manage cloud infrastructure and automate provisioning of resources. • Data Pipelines: Develop and manage data pipelines using AWS Glue for ETL (Extract, Transform, Load) processes, ensuring data is properly ingested, transformed, and available for analytics. • CI/CD: Implement and manage CI/CD pipelines to ensure rapid deployment of new features, bug fixes, and updates with minimal downtime. • API Design: Build and expose RESTful APIs for seamless communication between services and with front-end components. • Collaboration: Work closely with product managers, frontend engineers, and other stakeholders to deliver reliable, well-tested software features. • Security and Performance: Implement best practices for securing applications and ensure optimal performance through code optimizations, profiling, and monitoring. • Mentorship: Mentor junior developers, conduct code reviews, and help foster a culture of continuous learning and improvement. Skills & Qualifications: • Must-Have: o 5+ years of backend development experience with expertise in Python. o Strong experience with AWS services, including AWS Glue, Lambda, S3, EC2, RDS, and other AWS tools. o Proven experience in designing and managing data pipelines, particularly using AWS Glue. o Experience with Infrastructure as Code (IaC) using AWS CDK or similar frameworks (e.g., Terraform). o Strong knowledge of CI/CD processes and tools (e.g., Jenkins, GitLab CI, AWS CodePipeline). o Deep understanding of RESTful API design and experience working with APIs. o Familiarity with cloud security best practices (e.g., IAM, encryption, secure networking). o Strong debugging, performance tuning, and troubleshooting skills in distributed cloud environments. o Solid understanding of version control systems, particularly Git. o Excellent communication skills and the ability to collaborate effectively across teams. • Nice-to-Have: o Experience with AWS Glue, specifically in designing complex ETL pipelines. o Familiarity with event-driven architectures (e.g., using SNS, SQS, Kinesis). o Familiarity with containerization technologies (e.g., Docker, Kubernetes). o Experience with NoSQL databases like DynamoDB or MongoDB. o Prior exposure to serverless architectures, using services like AWS Lambda. o Understanding of data warehousing solutions and integration with AWS data lakes.