Enterprise Data Architect (Hybrid - Austin, TX)

Enterprise Data Architect
Location: Austin, TX (hybrid)

Schedule: Full-time | Hybrid (2–3 days in office)

Work Authorization Notice:
At this time, we are unable to provide immigration sponsorship for this position. Candidates must have current, unrestricted authorization to work in the country where the role is based.

About Us
Togetherwork is a $250M recurring revenue SaaS business with more than 33 software applications serving over 12 vertical markets. We help communities, organizations, and businesses thrive by delivering purpose-built software that supports their missions and operations.

Headquartered on South Congress in Austin, TX, Togetherwork is scaling rapidly. We are customer-focused, execution-driven, and committed to operational excellence. Our teams value accountability, collaboration, and continuous improvement. We are building a modern data foundation to support our next phase of growth.

About This Role
As an Enterprise Data Architect at Togetherwork, you are responsible for defining and executing the enterprise data strategy across our multi-product SaaS portfolio. This is a hands-on, technical leadership role focused on building scalable, secure, and analytics-ready AWS-native data platforms.

You will establish architectural standards while actively designing ingestion frameworks, pipelines, and data models. You partner closely with Engineering, Product, and executive stakeholders to modernize legacy systems, integrate acquired platforms, and enable BI and AI use cases through well-structured, governed data assets.

What You’ll Do

• Define and evolve the enterprise data architecture roadmap across our product portfolio
• Design AWS-native data architectures leveraging services such as S3, Redshift, Glue, Lambda, Kinesis, RDS, Aurora, and EMR
• Establish standards for ingestion, transformation, storage, modeling, governance, and security
• Design and implement batch and real-time ingestion pipelines across API, CDC, streaming, and batch patterns
• Build and optimize ETL and ELT workflows using AWS Glue, Lambda, Airflow, dbt, or similar frameworks
• Architect scalable data lakes and enterprise warehouse solutions
• Lead logical and physical data modeling, including dimensional, normalized, and Data Vault approaches
• Design canonical data models, semantic layers, and analytics-ready data marts
• Implement data governance frameworks aligned with AWS security best practices, including IAM and encryption standards
• Establish data quality monitoring, lineage tracking, and observability standards
• Optimize performance, reliability, and cost efficiency across distributed AWS data environments
• Mentor data engineers, conduct code reviews, and enforce best practices across teams
• Communicate architectural strategy, trade-offs, and investment priorities to technical and executive stakeholders

How You’ll Measure Success

• Adoption of a scalable, standardized enterprise data architecture across portfolio products
• Successful migration of legacy and acquired systems into a unified cloud-native data platform
• Improved data quality, governance, and security posture
• High-performing, cost-efficient pipelines with strong observability and reliability
• Enablement of BI, reporting, and AI initiatives through trusted, analytics-ready data assets
• Strong cross-functional alignment and measurable improvement in engineering standards

What You’ll Bring

• 8+ years of experience in data engineering or data architecture
• Proven experience designing and implementing enterprise-scale data platforms in AWS
• Deep hands-on expertise with AWS data services including S3, Redshift, Glue, Lambda, Kinesis, RDS/Aurora, and EMR
• Strong experience with cloud-native data lake and warehouse architectures
• Advanced SQL skills and strong proficiency in Python
• Experience building ETL and ELT pipelines and implementing workflow orchestration tools
• Strong understanding of IAM, networking, encryption, and AWS security best practices
• Experience optimizing distributed systems for performance and cost efficiency
• Experience implementing CI/CD practices for data engineering workflows
• Experience operating within multi-product or multi-tenant SaaS environments preferred

Why You’ll Love Working Here
At Togetherwork, we help community-driven organizations grow and thrive—creating better experiences for the people they serve. You will play a critical role in shaping the enterprise data foundation that powers our TW28 vision.

We are guided by values that shape how we work every day:

• Obsess over our customers
• Own it. Together.
• Move fast with purpose

The Company offers a comprehensive employee benefits program, including:

• Medical, dental, and vision insurance options
• 100% Employer paid short/long term disability
• Basic Life
• 401(k) option with 100% company match up to 4%
• Flexible paid personal/vacation time built on mutual trust and accountability
• 10 sick days annually
• 10 company paid holidays
• 6 weeks paid parental leave
• Pet Insurance
• Medical Travel Benefits
• Infertility Benefits
• Teladoc
• Employee Assistance Program
• Wellness Benefits & Engagement Platform

Inclusion and Diversity: Togetherwork is an Equal Employment Opportunity Employer. We are a company where diverse backgrounds, experiences, and viewpoints are valued. Togetherwork does not make hiring or employment decisions on the basis of race, color, religion, gender, gender identity, sex, sexual orientation, disability, veteran status, age, ethnic or national origin, or any other basis protected by all local, state or federal laws.

Interview Process & Expectations
Our interviews are designed to be interactive and conversational. Candidates are expected to respond based on their own experience and thinking during live interviews.

To ensure a fair and consistent interview experience, the use of real-time AI tools or other external assistance to generate or guide interview responses is not permitted. Interviews are intended to reflect an individual’s judgment, problem-solving approach, and communication.