Senior Data Architect Engineer (SaaS)

Posted 2 Days Ago
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New York, NY, USA
In-Office
Senior level
Information Technology • Productivity • Software • App development
The Role
Lead design and implementation of the enterprise data platform: architect scalable warehouse/lakehouse solutions, build and optimize ETL/ELT pipelines, enforce data quality and governance, tune analytical workloads, and enable reporting and self-service analytics in support of BI and AI initiatives.
Summary Generated by Built In

TeamBuilder is a rapidly growing healthcare SaaS company on a mission to transform healthcare with our innovative technology. We believe in empowering our customers through inventive solutions and a commitment to excellence. Our young, rapidly growing team is looking for passionate professionals who thrive in a dynamic, innovative, and collaborative environment.  **This role is fully remote, however you must reside in the US and be available on EST hours. 

Senior Data Architect/Engineer

Summary of Role 
We are seeking an experienced Senior Data Engineer / Data Architect to lead the
design, development, optimization, and evolution of our enterprise data platform.
This is a hands-on senior individual contributor role responsible for architecting
scalable data solutions, building robust data pipelines, optimizing analytical
workloads, and ensuring the delivery of trusted, high-quality data across the
organization. The successful candidate will combine strong technical expertise in
modern data engineering practices with the ability to design and govern
enterprise-scale data architectures.
You will work closely with software engineering, product, analytics, and business
stakeholders to deliver reliable, high-performance data solutions that support
operational reporting, business intelligence, advanced analytics, and AI
initiatives.
Key Responsibilities
Data Architecture & Platform Design
 Design and evolve the organization's data architecture to support
operational, analytical, and AI-driven workloads.
 Define and implement scalable data warehouse, data lake, and lakehouse
architectures.
 Establish data modelling standards, governance practices, and
architectural best practices.
 Design data structures and integration patterns that support long-term
scalability, performance, and maintainability.
 Drive data platform modernization initiatives leveraging Azure cloud
technologies.
Data Engineering
 Design, develop, and maintain scalable ETL/ELT pipelines that ingest,
transform, validate, and deliver data from multiple internal and external
systems.
 Build robust data integration solutions using Azure data services and
modern data engineering frameworks.
 Implement monitoring, alerting, and data quality controls to ensure
reliable data operations.
 Automate data processing workflows and reduce operational overhead
through engineering best practices.
Performance & Optimization
 Optimize SQL queries, data pipelines, and analytical workloads for
performance, scalability, and cost efficiency.
 Analyze and improve large-scale datasets and reporting environments.
 Identify bottlenecks and implement solutions that improve system
responsiveness and data availability.
 Partner with engineering teams to improve database design, indexing
strategies, and query performance.
Data Quality & Reconciliation
 Establish data validation, reconciliation, and auditing processes to ensure
trusted reporting outcomes.
 Investigate and resolve complex data discrepancies across source systems
and analytical platforms.
 Develop repeatable controls and monitoring processes that improve
confidence in business reporting.
 Define and maintain data quality standards across the platform.
Analytics Enablement
 Design and support data models that power reporting and analytics
solutions.
 Collaborate with business stakeholders to understand analytical
requirements and translate them into scalable data solutions.
 Support Power BI and custom analytics platforms through semantic
modelling, data optimization, and performance tuning.
 Enable self-service analytics through well-designed and governed datasets.
Required Qualifications
 7+ years of experience in Data Engineering, Data Architecture, or related
disciplines.
 Strong experience designing and implementing enterprise-scale data
architectures.
 Expert-level SQL skills with proven experience optimizing complex queries
and large datasets.
 Extensive experience building ETL/ELT solutions in cloud-based
environments.
 Strong understanding of data warehousing, dimensional modelling, and
analytical data design.
 Experience implementing data quality, governance, lineage, and
reconciliation processes.
 Strong analytical and problem-solving skills with the ability to diagnose
complex data issues.
 Excellent written and verbal communication skills.
 Ability to work independently and collaborate effectively across technical
and non-technical teams.
Technical Skills
Required
 Azure SQL Database
 Azure Data Factory
 Azure Synapse Analytics and/or Azure Databricks
 SQL Server and advanced SQL optimization
 Data Warehouse and Lakehouse design
 ETL/ELT architecture and implementation
 Data modelling (Star Schema, Snowflake, Dimensional Modelling)
 Performance tuning and query optimization
 Data quality and reconciliation frameworks
 Power BI data modelling and performance optimization
Preferred
 Microsoft Fabric
 Azure Data Lake Storage
 Apache Spark
 Python
 CI/CD for data platforms
 Infrastructure as Code
 Real-time and streaming data architectures
 AI and machine learning data platform experience
Preferred Experience
 Experience supporting SaaS platforms with large-scale operational and
analytical datasets.
 Experience working in healthcare, workforce management, scheduling, or
other data-intensive industries.
 Experience designing data platforms that support advanced analytics,
forecasting, optimization, or AI initiatives.
 Experience delivering enterprise reporting and business intelligence
solutions.
What Success Looks Like
 Reliable, scalable, and well-governed data platforms.
 Trusted reporting supported by strong data quality and reconciliation
processes.
 High-performance analytical environments that scale with business
growth.
 Reduced operational overhead through automation and platform
improvements.
 Data architectures that enable future analytics and AI initiatives.
 Strong collaboration with engineering, product, and business teams to
drive measurable business outcomes.

Additional Information 

  1. Job Type: Full-time, Exempt, Remote primarily East Coast time zone, Some Travel Required 
  2. Compensation: Competitive including paid time off, medical benefits, and the potential for an annual performance bonus, and/or equity 
  3. We foster a collaborative, engaging, mission-driven culture that values innovation and prioritizes customer success. We like to have fun together and support each other too! 

Skills Required

  • 7+ years of experience in Data Engineering, Data Architecture, or related disciplines
  • Experience designing and implementing enterprise-scale data architectures
  • Expert-level SQL skills with proven experience optimizing complex queries and large datasets
  • Extensive experience building ETL/ELT solutions in cloud-based environments
  • Strong understanding of data warehousing, dimensional modelling, and analytical data design
  • Experience implementing data quality, governance, lineage, and reconciliation processes
  • Strong analytical and problem-solving skills with the ability to diagnose complex data issues
  • Excellent written and verbal communication skills
  • Ability to work independently and collaborate effectively across technical and non-technical teams
  • Azure SQL Database
  • Azure Data Factory
  • Azure Synapse Analytics and/or Azure Databricks
  • SQL Server and advanced SQL optimization
  • Data Warehouse and Lakehouse design
  • ETL/ELT architecture and implementation
  • Data modelling (Star Schema, Snowflake, Dimensional Modelling)
  • Performance tuning and query optimization
  • Data quality and reconciliation frameworks
  • Power BI data modelling and performance optimization
  • Microsoft Fabric
  • Azure Data Lake Storage
  • Apache Spark
  • Python
  • CI/CD for data platforms
  • Infrastructure as Code
  • Real-time and streaming data architectures
  • AI and machine learning data platform experience
  • Experience supporting SaaS platforms with large-scale operational and analytical datasets
  • Experience working in healthcare, workforce management, scheduling, or other data-intensive industries
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The Company
HQ: Newtown, CT
17 Employees
Year Founded: 2021

What We Do

Designed specifically for ambulatory healthcare, TeamBuilder uses leading data science to empower leaders to predict and balance workloads by recommending optimal staff schedules. TeamBuilder unlocks workforce productivity, improves the user experience, and delivers actionable insights uncovering significant opportunities for efficiency. Visit our Careers Page for open opportunities! https://teambuilder.applytojob.com/apply

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