Design, build, maintain, analyze, and interpret data to provide actionable insights that drive business decisions. Collaborate with cross-functional teams to understand data requirements and design solutions that meet business needs.
Requirements
- Design and maintain upstream data pipelines that deliver analytics-ready datasets optimized for Tableau reporting.
- Develop and optimize data models (e.g., star schema, snowflake, extracts) to support efficient Tableau dashboards and self-service analytics.
- Manage Tableau Server/Cloud administration, including permissions, row-level security, publishing workflows, and content governance.
- Implement end-to-end testing and validation to ensure consistency and accuracy between source data, pipelines, and Tableau dashboards.
- Leverage advanced Tableau capabilities such as parameters, level-of-detail (LOD) expressions, and complex calculations to meet sophisticated reporting needs.
- Design, develop, and maintain data solutions for data generation, collection, and processing
- Create data pipelines and ensure data quality by implementing ETL processes to migrate and deploy data across systems
- Contribute to the design, development, and implementation of data pipelines, ETL/ELT processes, and data integration solutions
- Take ownership of data pipeline projects from inception to deployment, manage scope, timelines, and risks
- Collaborate with cross-functional teams to understand data requirements and design solutions that meet business needs
- Develop and maintain data models, data dictionaries, and other documentation to ensure data accuracy and consistency
- Implement data security and privacy measures to protect sensitive data
- Leverage cloud platforms (AWS preferred) to build scalable and efficient data solutions
- Collaborate and communicate effectively with product teams
- Collaborate with Data Architects, Business SMEs, and Data Scientists to design and develop end-to-end data pipelines to meet fast-paced business needs across geographic regions
- Identify and resolve complex data-related challenges
- Adhere to best practices for coding, testing, and designing reusable code/component
- Explore new tools and technologies that will help to improve ETL platform performance
- Participate in sprint planning meetings and provide estimations on technical implementation
- Design and develop data pipelines leveraging Databricks, PySpark, and SQL to ingest, transform, and process large-scale datasets.
- Engineer solutions for both structured and unstructured data to enable advanced analytics and insights.
- Implement automated workflows for data ingestion, transformation, and deployment using Databricks Jobs and notebooks, with ongoing monitoring and scheduling.
- Apply performance optimization techniques, including Spark job tuning, caching, partitioning, and indexing, to improve scalability and efficiency.
- Build integrations with multiple data sources, such as SQL databases, APIs, and cloud storage platforms, ensuring seamless connectivity and reliability.
- Collaborate effectively with global teams across time zones to maintain alignment, resolve issues, and deliver on shared objectives.
Benefits
- Generous Paid Time Off
- 401k Matching
- Retirement Plan
- Visa Sponsorship
- Four Day Work Week
- Generous Parental Leave
- Tuition Reimbursement
- Relocation Assistance
