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Data Engineer — Cloud Data Pipelines & WarehousingBuilding Service 32BJ Benefit Funds • New York, New York, United States
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Data Engineer — Cloud Data Pipelines & Warehousing

Building Service 32BJ Benefit Funds
  • US
    New York, New York, United States
  • US
    New York, New York, United States
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À propos

About Us 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. The Funds oversees and manages $9 billion of assets from property owners, requiring highly skilled financial management professionals.
Job Summary As a Data Engineer you will play a key collaborative role in delivering powerful data-driven products that support 32BJ Health Fund’s mission of providing high-quality, low-cost healthcare to union members. You will provide internal analysts with accurate datasets by implementing best practices in data collection, movement, storage, and transformation of large datasets. You will work with current ETL/Data Warehousing and guide future development of data storage, streaming, and pipeline architectures.
Essential Duties and Responsibilities
Work with Health Fund Analytics, Operations, and IT to design, develop, maintain, and optimize complex data pipelines supporting both on‑premises and Azure cloud environments.
Migrate and integrate data from disparate internal and external sources into centralized Azure cloud and on‑premises data warehouse solutions using established data engineering best practices.
Use 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.
Design and implement resilient ingestion pipelines capable of handling schema drift, missing or invalid fields, inconsistent vendor formats, and evolving source system structures.
Build scalable, flexible, and extensible data models that support evolving business requirements, onboarding of new vendors, and downstream analytics/reporting needs.
Implement and maintain medallion architecture principles with clear separation of raw, refined, and curated data layers.
Diagnose and resolve performance bottlenecks impacting pipeline efficiency, reporting processes, and downstream data consumers across SQL Server and Databricks environments.
Support and optimize data workflows across hybrid on‑premises and cloud‑based platforms during ongoing cloud migration initiatives.
Translate operational and business requirements into scalable, maintainable, and efficient data engineering solutions.
Anticipate and mitigate risks related to vendor data variability, schema evolution, and data quality issues to ensure data reliability and continuity.
Prioritize and manage technical debt to improve platform stability, maintainability, scalability, and delivery efficiency.
Generate data subsets, semantic models, APIs, variables, and reusable datasets required for integration with internal applications, analytics tools, and public‑facing platforms.
Collaborate with IT and Operations teams to evaluate, implement, and support scalable cloud‑based solutions, including Azure and Dynamics 365 technologies.
Support implementation and ongoing maintenance of enterprise Data Governance policies, standards, and data management best practices within assigned domains.
Support data engineering operations through proactive monitoring, alerting, troubleshooting, debugging, and maintenance activities to minimize downtime and ensure data quality.
Interface with internal stakeholders and external vendor IT teams to resolve data quality issues and ensure HIPAA‑compliant data handling, transfer, and storage practices.
Create and maintain clear technical documentation, including data dictionaries, schemas, user guides, quick‑start materials, and workflow/process documentation.
Provide training sessions, tutorials, and ongoing support to analysts and business users on data access, query development, reporting tools, and available data resources.
Serve as a subject matter expert on internal and external data sources, data architecture, and enterprise data management practices.
Qualifications (Competencies)
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.
Soft Skills (Interpersonal Skills)
Ability to communicate technical concepts and trade‑offs 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.
Education 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.
Language Skills Strong verbal and written communication skills in English, including the ability to read, write, understand, and effectively communicate technical and business information.
Physical Demands
Under 1/3 of the time: Standing, walking, climbing or balancing, stooping, kneeling, crouching, or crawling.
Over 2/3 of the time: Talking or hearing.
100 % of the time: Using hands.
Work Environment Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions of this job.
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  • New York, New York, United States

Compétences linguistiques

  • English
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