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TeamBuilder

Senior Data Architect Engineer (SaaS)

Posted 3 Days Ago
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In-Office
New York, NY, USA
Senior level
In-Office
New York, NY, USA
Senior level
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.
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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! 

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