32bjfunds4d ago

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Data Engineer

United States·New YorkFull Timemid

Data EngineerData

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Quick Summary

Key Responsibilities

Works with Health Fund Analytics, Operations, and IT to design, develop, maintain,

Technical Tools

Data EngineerData

Building Services 32BJ Benefit Funds (“the Funds”) is the umbrella organization responsible for administering Health, Pension, Retirement Savings, Training, and Legal Services benefits to over 100,000 SEIU 32BJ members. Our mission is to make significant contributions to the lives of our members by providing high quality benefits and services. Through our commitment, we embody five core values: Flexibility, Initiative, Respect, Sustainability, and Teamwork (FIRST). By following our core values, employees are open to different and new ways of doing things, take active steps to improve the organization, create an environment of trust and respect, approach their work with the intent of a positive outcome, and work collaboratively with colleagues.

What We Offer

~1 min read

As a Data Engineer you will get to play a key and a collaborative role in the delivery of powerful data-driven products that support 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.

Responsibilities

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→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

Requirements

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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

Bachelor’s degree in Computer Science, Information Systems, Data Science, Engineering, or a related technical field, or equivalent combination of education and hands-on experience

Strong verbal and written communication skills in English, including the ability to read, write, understand, and effectively communicate technical and business information.

Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions.