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Module Lead – Data Bricks
YASH Technologies. Design, develop, and maintain scalable data pipelines using AWS services .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and maintaining scalable data pipelines using AWS services, with a strong focus on ETL/ELT workflows, data governance, and performance optimization. Proficient in developing data lakes, data warehouses, and reusable frameworks for data orchestration and deployment.
Highest-signal resume keywords
AWS Data Pipeline DevelopmentETL/ELT Workflow OptimizationData Lake and Data Warehouse ManagementData Governance and Compliance ImplementationAWS Databricks Data Ingestion
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data Pipeline DesignETL/ELT WorkflowsData GovernanceData Quality ControlData ModelingData TransformationData OrchestrationCI/CD ImplementationStatistical TechniquesMachine Learning Techniques
Soft Skills
Analytical SkillsProblem-SolvingCommunication SkillsRequirement AnalysisFeedback Provision
Tools & Technologies
AWS ServicesAWS DatabricksData Science ToolsArchitecture FrameworksSDLC Methodologies
Industry Keywords
Data GovernanceData QualityData SecurityData ComplianceData Integration
Tech Stack
Tools & technologiesAWSETLSDLC
About the role
Key responsibilities & impact- Design, develop, and maintain scalable data pipelines using AWS services
- Build and optimize ETL/ELT workflows for structured and unstructured data
- Implement data ingestion solutions using AWS Databricks
- Develop and manage data lakes and data warehouse solutions on AWS
- Create and maintain data models, transformations, and processing frameworks
- Implement data governance, data quality, security, and compliance controls
- Monitor and optimize data platform performance, scalability, and cost
- Develop reusable frameworks and automation for data orchestration and deployment
- Troubleshoot data-related issues and provide production support
- Build Bronze, Silver, and Gold data assets
- Develop reusable enterprise data products
- Create pipelines, transformations, and data integrations
- Implement CI/CD, monitoring, and operational support of use cases through MVP
- Operationalize MVP solutions into production-ready assets
- Gather and analyze requirements, assess change impacts, and identify dependencies
- Implement code or configure/customize products and provide design and architecture inputs
- Analyze frameworks and tools, review code, and provide feedback on improvement opportunities
- Provide architectural design and documentation and implement architectural patterns
- Identify causes of errors and potential solutions
Requirements
What you’ll need- 5-8 years of experience
- Familiarity with Iceberg Tables
- Experience designing, developing, and maintaining scalable data pipelines using AWS services
- Experience building and optimizing ETL/ELT workflows for structured and unstructured data
- Experience implementing data ingestion solutions using AWS Databricks
- Experience developing and managing data lakes and data warehouse solutions on AWS
- Experience creating and maintaining data models, transformations, and processing frameworks
- Experience implementing data governance, data quality, security, and compliance controls
- Experience monitoring and optimizing data platform performance, scalability, and cost
- Experience developing reusable frameworks and automation for data orchestration and deployment
- Experience troubleshooting data-related issues and providing production support
- Knowledge of customer business processes and relevant technology platforms or products
- Working knowledge of requirement management and requirement analysis processes, tools, and methodologies
- Working knowledge of technology product/platform standards and specifications
- Working knowledge of architecture industry tools and frameworks
- Working knowledge of architectural elements, SDLC, and methodologies
- Knowledge of statistical and machine learning techniques including classification, linear regression modelling, clustering, and decision trees
- Familiarity with mainstream commercial and open-source data science/analytics software tools
- Ability to prepare process maps, workflows, business cases, and simple business models
- Ability to analyze change impacts and identify dependencies among requirements
- Ability to implement code or configure/customize products according to industry standards
- Ability to analyze frameworks/tools, review code, and provide improvement feedback
- Ability to identify appropriate tools and frameworks for customer requirements
- Ability to provide architectural design/documentation at application or function capability level
- Ability to identify causes of errors and potential solutions
- Mandatory certification requirement is listed, but no specific certification is named
Benefits
Comp & perks- Flexible work arrangements
- Inclusive team environment
- Career-oriented skilling models
- Continuous learning, unlearning, and relearning
- Agile self-determination, trust, transparency, and open collaboration
- Support needed for realization of business goals
- Stable employment
- Great atmosphere and ethical corporate culture