Senior Data Architect Engineer (SaaS)

TeamBuilder•New York, NY

6d•Remote

About The Position

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. 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.

Requirements

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.
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

Nice To Haves

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.
Experience designing data platforms that support advanced analytics, forecasting, optimization, or AI initiatives.
Experience delivering enterprise reporting and business intelligence solutions.

Responsibilities

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.
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.
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.
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.
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.