Data Engineer

BLDG SVC 32 B-J•New York, NY

18d•$100,000 - $115,000

About The Position

As a Data Engineer, you will play a key and collaborative role in the delivery of powerful data-driven products that support the 32BJ Health Fund's mission of providing high-quality and low-cost healthcare to its union members. The Data Engineer will be responsible for providing internal analysts with accurate datasets by implementing best practices in data collection, movement, storage, and transformation of large datasets. This individual will work with both current ETL/Data Warehousing and provide direction for future development of data storage, streaming, and pipeline architectures.

Requirements

1–3 years of professional experience in data engineering, data integration, data warehousing, analytics engineering, or related technical disciplines
Demonstrated ability to support scalable data pipelines, data transformation processes, and cloud-based data initiatives within collaborative technical environments
Strong understanding of software engineering best practices applied to data engineering, including modular design, automated testing, CI/CD, version control, and idempotent processing
Advanced SQL development skills, including stored procedures, functions, triggers, query optimization, indexing strategies, and performance troubleshooting across large-scale datasets
Strong understanding of modern data engineering concepts and architecture, including ETL/ELT frameworks, pipeline orchestration, data modeling, schema evolution, batch ingestion patterns, and medallion architecture principles within Databricks/Delta Lake environments
Proficiency in Python (preferred) for developing scalable, maintainable, and production-ready data pipelines within Databricks
Experience working within the Azure ecosystem, especially Azure Databricks, Delta Lake, and cloud migration initiatives
Experience working within Agile/DevOps delivery environments and cross-functional technical teams
Prior experience working with healthcare claims data and understanding healthcare/benefits domain concepts, including claims, eligibility, providers, and benefit fund cost drivers
Ability to communicate technical concepts and tradeoffs effectively to both technical and non-technical stakeholders
Strong collaboration and participation within Agile/DevOps teams
Comfort working in ambiguous, fast-paced, and evolving environments
Ownership mentality with proactive identification of risks, inefficiencies, and improvement opportunities
Pragmatic decision-making and ability to balance ideal architecture with delivery timelines
Continuous learning mindset and adaptability to new technologies and platforms
Mission-oriented approach focused on improving operational efficiency and member outcomes
Strong verbal and written communication skills in English, including the ability to read, write, understand, and effectively communicate technical and business information.

Responsibilities

Works with Health Fund Analytics, Operations, and IT to design, develop, maintain, and optimize complex data pipelines supporting both on-premises and Azure cloud environments
Migrates and integrates data from disparate internal and external sources into centralized Azure cloud and on-premises data warehouse solutions using established data engineering best practices
Uses SQL, Azure Data Factory, Databricks, Python, and other data transformation tools to develop and automate scalable ETL/ELT processes for ingesting, transforming, and loading data from multiple vendors into centralized data platforms
Designs and implements resilient ingestion pipelines capable of handling schema drift, missing or invalid fields, inconsistent vendor formats, and evolving source system structures
Builds scalable, flexible, and extensible data models that support evolving business requirements, onboarding of new vendors, and downstream analytics/reporting needs
Implements and maintains medallion architecture principles with clear separation of raw, refined, and curated data layers
Diagnoses and resolves performance bottlenecks impacting pipeline efficiency, reporting processes, and downstream data consumers across SQL Server and Databricks environments
Supports and optimizes data workflows across hybrid on-premises and cloud-based platforms during ongoing cloud migration initiatives
Translates operational and business requirements into scalable, maintainable, and efficient data engineering solutions
Anticipates and mitigates risks related to vendor data variability, schema evolution, and data quality issues to ensure data reliability and continuity
Prioritizes and manages technical debt to improve platform stability, maintainability, scalability, and delivery efficiency
Generates data subsets, semantic models, APIs, variables, and reusable datasets required for integration with internal applications, analytics tools, and public-facing platforms
Works collaboratively with IT and Operations teams to evaluate, implement, and support scalable cloud-based solutions, including Azure and Dynamics 365 technologies
Supports implementation and ongoing maintenance of enterprise Data Governance policies, standards, and data management best practices within assigned domains
Supports data engineering operations through proactive monitoring, alerting, troubleshooting, debugging, and maintenance activities to minimize downtime and ensure data quality
Interfaces with internal stakeholders and external vendor IT teams to resolve data quality issues and ensure HIPAA-compliant data handling, transfer, and storage practices
Creates and maintains clear technical documentation, including data dictionaries, schemas, user guides, quick-start materials, and workflow/process documentation
Provides training sessions, tutorials, and ongoing support to analysts and business users on data access, query development, reporting tools, and available data resources
Serves as a subject matter expert on internal and external data sources, data architecture, and enterprise data management practices