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Senior Data Architect Engineer (SaaS) - TeamBuilder - Career Page Skip To Job Description

Thanks for your interest in TeamBuilder! We are seeking candidates with a diverse range of perspectives, ideas and experience. If you are interested in joining us Join us at the intersection of healthcare operations, analytics, and SaaS technology, we'd love to hear from you!

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Senior Data Architect Engineer (SaaS)

New York, NY

Contracted to Full Time

Experienced

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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

• Job Type: Full-time, Exempt, Remote primarily East Coast time zone, Some Travel Required
• Compensation: Competitive including paid time off, medical benefits, and the potential for an annual performance bonus, and/or equity
• 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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