Responsible for maintaining and enhancing data warehouses and pipelines, developing scalable ETL processes, data analysis, and reporting through visualization tools like PowerBI.
Job Overview
We are seeking a skilled Data Engineer to join our team and drive our data infrastructure forward. In this role, you will primarily focus on maintaining and enhancing our data warehouse and pipelines (80%) while also contributing to data analysis and reporting initiatives (20%). You'll work closely with cross-functional stakeholders to build robust data solutions and create actionable insights through compelling visualizations.
Key Responsibilities
Data Engineering
- Infrastructure Management: Maintain, enhance, and optimize existing data warehouse architecture and ETL pipelines.
- Pipeline Development: Design and implement scalable ETL/ELT processes ensuring data quality, integrity, and timeliness.
- Performance Optimization: Monitor and improve pipeline performance, troubleshoot issues, and implement best practices.
- Documentation: Create and maintain comprehensive documentation for data engineering processes, architecture, and configurations.
Data Analysis & Reporting
- Stakeholder Collaboration: Partner with business teams to gather requirements and translate them into technical solutions.
- Report Development: Build and maintain PowerBI dashboards and reports that drive business decisions.
- Data Modeling: Develop new data models and enhance existing ones to support advanced analytics.
- Insight Communication: Transform complex data findings into clear, actionable insights for various departments.
Required Qualifications
Technical Skills
- Programming & Query Languages: Strong proficiency in Python, SQL, and PySpark.
- Big Data Platforms: Experience with cloud data platforms including Snowflake, BigQuery, and Databricks. Databricks experience highly preferred.
- Orchestration Tools: Proven experience with workflow orchestration tools (Airflow preferred).
- Cloud Platforms: Experience with AWS (preferred), Azure, or Google Cloud Platform.
- Data Visualization: Proficiency in PowerBI (preferred) or Tableau.
- Database Systems: Familiarity with relational database management systems (RDBMS).
Development Practices
- Version Control: Proficient with Git for code management and collaboration.
- CI/CD: Hands-on experience implementing and maintaining continuous integration/deployment pipelines.
- Documentation: Strong ability to create clear technical documentation.
Experience & Communication
- Professional Experience: 3+ years in data engineering or closely related roles.
- Language Requirements: Fluent English communication skills for effective collaboration with U.S. based team members.
- Pipeline Expertise: Demonstrated experience building and maintaining production data pipelinesk
Similar Jobs
Blockchain • eCommerce • Fintech • Payments • Software • Financial Services • Cryptocurrency
Lead product data engineering across architecture, data modeling, pipelines, monitoring, lineage, and data quality. Build reliable analytical datasets, support experimentation and machine learning use cases, and resolve business-critical pipeline issues. Partner with product and engineering stakeholders to translate business needs into automated data solutions. The role requires ownership of end-to-end data engineering projects, on-call participation, and development of AI-assisted monitoring and automation.
Top Skills:
AirflowDatabricksDbtGitOmniPrefectPythonSnowflakeSQLTerraform
Blockchain • Fintech • Mobile • Payments • Software • Financial Services
Lead product data engineering initiatives by designing data models, owning critical pipelines, building data quality and lineage monitoring, and supporting experimentation and ML-ready datasets. Translate business needs into automated data solutions, maintain pipeline SLAs through on-call support, and develop AI-assisted tools for issue detection and resolution. Collaborate with Product, Engineering, Data Science, Machine Learning, and Applied AI teams across Block’s products.
Top Skills:
AirflowDatabricksDbtGitOmniPrefectPythonSnowflakeSQLTerraform
.png)
Consumer Web • eCommerce • Machine Learning • Software • Sports • Analytics
Build and own scalable data platforms and ingestion pipelines using BigQuery, dbt, and modern ingestion tools. Design batch and CDC workflows, improve reliability and observability, implement governance and access controls, and support analytics and AI agents. Develop Python services and internal tooling, contribute to architecture and lakehouse experiments, collaborate with stakeholders, and mentor engineers. The role requires strong SQL, Python, cloud infrastructure, testing, CI/CD, and AI-assisted development experience.
Top Skills:
Ai Coding AgentsAmazon Web ServicesAPIsCdcCi/CdClaude CodeDbtEstuaryGitGoogle BigqueryGoogle Cloud PlatformHevoLakehouseMcpOpen Table FormatsPythonSQL
What you need to know about the Austin Tech Scene
Austin has a diverse and thriving tech ecosystem thanks to home-grown companies like Dell and major campuses for IBM, AMD and Apple. The state’s flagship university, the University of Texas at Austin, is known for its engineering school, and the city is known for its annual South by Southwest tech and media conference. Austin’s tech scene spans many verticals, but it’s particularly known for hardware, including semiconductors, as well as AI, biotechnology and cloud computing. And its food and music scene, low taxes and favorable climate has made the city a destination for tech workers from across the country.
Key Facts About Austin Tech
- Number of Tech Workers: 180,500; 13.7% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Dell, IBM, AMD, Apple, Alphabet
- Key Industries: Artificial intelligence, hardware, cloud computing, software, healthtech
- Funding Landscape: $4.5 billion in VC funding in 2024 (Pitchbook)
- Notable Investors: Live Oak Ventures, Austin Ventures, Hinge Capital, Gigafund, KdT Ventures, Next Coast Ventures, Silverton Partners
- Research Centers and Universities: University of Texas, Southwestern University, Texas State University, Center for Complex Quantum Systems, Oden Institute for Computational Engineering and Sciences, Texas Advanced Computing Center
