Data and Analytics Engineer – Corporate GenAI
| Company | Visa |
|---|---|
| Location | Austin, TX, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Bachelor’s, Master’s, MBA, JD, MD |
| Experience Level | Senior |
Requirements
- 5+ years of relevant work experience with a Bachelor’s Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD, OR 8+ years of relevant work experience.
- Proficient in SQL and Python; experienced with data lakes, warehouses, ETL/ELT pipelines, data modeling, and governance; skilled in building automated data pipelines and reporting systems.
Responsibilities
- Design, develop, and maintain robust ETL/ELT pipelines using modern data engineering tools and frameworks.
- Build and optimize data lakes and data warehouses.
- Ensure data quality, consistency, and security across all data platforms.
- Implement data governance best practices and scalable data models.
- Design and maintain interactive dashboards and executive-level reports using tools like Tableau and Power BI to deliver actionable insights and support strategic decision-making.
- Collaborate with stakeholders to define KPIs and translate complex data into clear visual narratives, supporting GenAI-driven insights and business objectives.
- Provide visualizations of key product metrics such as product usage, user adoption, product maturity, and customer satisfaction to guide product and customer strategy.
- Partner with architects, analysts, and business stakeholders to understand data needs and deliver engineering solutions.
- Support executive reporting and analytics by enabling reliable data access and transformation.
- Monitor data pipeline performance and implement improvements for scalability and reliability.
- Automate recurring data processes and reporting workflows to enhance operational efficiency.
- Develop scalable data pipelines and workflows using various data sources and platforms.
- Build and manage scheduled reporting systems to ensure timely delivery of recurring insights.
- Design and implement robust ETL/ELT processes to support analytics and GenAI use cases.
- Provide comprehensive, high-level insights through dashboards tailored for leadership and executive stakeholders.
- Optimize data models and queries for performance, reliability, and maintainability.
- Integrate data engineering solutions with BI tools and platforms.
- Collaborate with DevOps and cloud teams to manage infrastructure-as-code and CI/CD pipelines.
- Monitor and analyze key metrics to support continuous improvement.
- Explore opportunities for automation and process enhancement in data reporting and analytics.
- Continuously evaluate and adopt new tools, frameworks, and best practices.
- Conduct root cause analysis and implement solutions to improve data reliability.
- Participate in code reviews and knowledge-sharing sessions.
Preferred Qualifications
- Proficient with data lakes and warehouses (e.g., Snowflake, BigQuery, Redshift)
- Strong skills in data modeling, ETL/ELT pipelines, and data governance
- Experience building automated data pipelines and reporting systems for BI and GenAI use cases
- Expertise in BI tools (e.g., Tableau, Power BI) and data transformation tools (e.g., Tableau Prep, Power Query, DAX, M)
- Familiarity with low-code platforms, automation tools, and API integrations
- Understanding of GenAI concepts, LLM tools, prompt engineering, and supporting infrastructure
- Experience with real-time data processing (e.g., Kafka, Spark Streaming)
- Knowledge of vector databases, semantic search, and embedding models
- Familiarity with CI/CD pipelines, infrastructure-as-code, and containerization (e.g., Docker, Kubernetes)
- Proficiency in Agile methodologies and tools (e.g., JIRA)
- Understanding of data security, encryption, and compliance standards
- Ability to work with structured, semi-structured, and unstructured data
- Strong communication and collaboration skills across cross-functional teams