Similar Jobs
Financial Services
Develop and modernize scalable batch and real-time data pipelines and feature engineering solutions on Databricks. Build feature store capabilities, migrate legacy Spark and EMR workloads, implement cloud-native AWS solutions, and apply testing, CI/CD, observability, governance, security, and data-quality controls. Collaborate with data scientists and stakeholders, contribute to architecture decisions, support production systems, and mentor junior engineers.
Top Skills:
Amazon AuroraAmazon EcsAmazon EksAmazon RdsAmazon S3SparkAWSAws GlueAws LambdaCi/CdDatabricksDelta LakeDelta Live TablesEmrGithub CopilotKafkaLakeflowLlm SuitePysparkPythonSnowflakeSQLTerraformUnity Catalog
Fintech • Information Technology • Payments • Sharing Economy • Financial Services • Cryptocurrency
Designs, builds, administers, and supports cloud data architectures, databases, schemas, metadata repositories, ETL processes, and analytic models. Leads data quality, governance, troubleshooting, testing, automation, reporting, dashboards, and infrastructure design. Collaborates with Agile teams to translate business requirements into technical capabilities and supports decision-making for Audit and business management. The role is full-time onsite at an eligible Federal Reserve location and requires extensive security screening.
Top Skills:
Amazon Aurora PostgresqlAmazon EbsAmazon S3AribaAWSAws GlueAws IamAws LambdaAws MonitoringAws NetworkingGitlab Ci/CdJavaJava EePythonScalaSQLTerraformWorkday AdaptiveWorkday Prism
.png){kind=logo}
AdTech • eCommerce • Information Technology • Software • Travel • Generative AI
Build and operate scalable data pipelines and trustworthy data models supporting Layla’s AI-powered travel products. Own data quality, reliability, observability, experimentation, and model-evaluation foundations. Enable production AI/ML systems through feature and data availability, evaluation datasets, and monitoring. Partner with Product, Analytics, and ML teams to deliver dependable insights, establish engineering best practices, and mentor other engineers.
Top Skills:
BigQueryClaudeDbtLookerMetabasePythonSnowflakeSQL
Design, build, operate, and support ETL/ELT pipelines, integrations, and curated datasets across enterprise systems (Salesforce, Deltek, Autodesk, Azure, Microsoft Fabric). Implement data models, orchestration, API-first services, data quality, governance, and monitoring to support BI, reporting, AI/ML, and secure data delivery.
The Data Engineer designs, builds, and supports the data pipelines, integrations, and curated datasets that power analytics, reporting, and AI initiatives for PBK’s Architecture vertical and broader AEC operations. This role owns integrations across enterprise platforms, including Salesforce, Deltek Vantagepoint, Autodesk data sources, Azure, and Microsoft Fabric. The Data Engineer ensures data is reliable, governed, secure, and available to support business visibility, project delivery, automation, and decision-making.
Your Impact:
- Design, build, operate, and support ETL/ELT pipelines and integrations across enterprise systems, including Salesforce, Deltek Vantagepoint, Autodesk data sources, Azure, and Microsoft Fabric.
- Implement data models, curated datasets, Lakehouse/Warehouse structures, and transformation layers to support BI, reporting, AI, and machine learning use cases.
- Build and support API-first data services, data contracts, and integration workflows using REST, JSON, SOAP where applicable, webhooks, and related patterns.
- Develop orchestration and scheduling processes using Microsoft Fabric Data Pipelines, Azure Data Factory, Azure Logic Apps, Functions, Spark notebooks, dbt, or similar tools.
- Apply event-driven integration patterns using Event Grid, Service Bus, Kafka concepts, or similar technologies where appropriate.
- Own the operational reliability of pipelines and integrations, including monitoring, alerting, incident response, troubleshooting, root-cause analysis, and remediation.
- Implement data quality checks, validation, lineage, documentation, and governance practices to ensure data is accurate, trusted, and usable.
- Partner with analytics, application, IT/Security, and business teams to support secure access, data governance, compliance, and reliable data delivery.
- Support AI initiatives by preparing reliable datasets, including AI/ML pipelines or RAG-oriented datasets where applicable.
- Use AI tools responsibly to improve productivity, accelerate development, support testing, and improve documentation while maintaining security and quality standards.
Here’s What You’ll Need:
- 5+ years of experience designing, building, and operating production data pipelines and integrations.
- Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Engineering, or a related field; directly relevant experience, certifications, or training demonstrating comparable expertise in data pipelines, integrations, cloud data platforms, APIs, data governance, and secure data workflows may be considered in lieu of a degree.
- Strong SQL skills and experience with PostgreSQL or similar relational databases.
- Proficiency in Python for data engineering, automation, and workflow development.
- Hands-on experience with Microsoft Azure data services and Microsoft Fabric, or equivalent cloud data platforms.
- Experience with ETL/ELT pipelines, data modeling, curated datasets, orchestration, and production data workflows.
- Strong experience with APIs and integration patterns, including REST, webhooks, authentication, error handling, retry strategies, and cross-system data movement.
- Familiarity with SDLC and CI/CD for data workflows using GitHub Enterprise, Azure DevOps, or similar tools.
- Strong understanding of security, data governance, access controls, and secure handling of sensitive business information.
- Ability to communicate clearly with technical and non-technical stakeholders in an AEC or similar business environment.
- Operational discipline with the ability to maintain, monitor, troubleshoot, and improve mission-critical data flows over time.
Here’s How You’ll Stand Out:
- Experience integrating enterprise platforms such as Salesforce, Deltek Vantagepoint, Autodesk Forma, Autodesk data management services, Microsoft 365, or similar systems.
- Experience with Azure Logic Apps, Azure Functions, MuleSoft, Databricks, Snowflake, or other iPaaS/middleware and cloud data platforms.
- Experience with dbt, Airflow, Databricks Workflows/Jobs, Fabric notebooks, Spark, or similar transformation and orchestration frameworks.
- Experience with streaming or event-driven integrations using Event Grid, Service Bus, Kafka concepts, or similar technologies.
- Experience with Power BI semantic models, performance optimization, and BI-ready dataset design.
- Experience supporting AI/ML pipelines, RAG-oriented datasets, or data products used in AI-enabled workflows.
- Strong integration owner mindset with the ability to build solutions that are maintainable, observable, secure, and supportable.
- Business-oriented approach with a focus on data outcomes that improve delivery, visibility, automation, and decision-making.
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
