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AirLife

Data Engineer

Grand Rapids, MI, United States

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Job details

Posting language: English

Original job description

COMPANY DESCRIPTION

At AirLife, we are dedicated to improving the quality of every breath. Excellence with Every Breath is not just a tag line, but the way we work and take care of our customers. With a mindset to evolve, innovate, and grow, we are a premier manufacturer of the highest-quality and market-leading breathing consumables. This growth philosophy has positioned us to increase our global footprint and business reach, impacting even more people around the world. Our expanding family of the most trusted brands offers a product portfolio that spans the continuum of care from first responder to home care, with safety, patient comfort, and clinical performance in mind. Collective expertise allows us to provide quality products and experiences to our patients, customers, and our people. Our values of Customer first, Differentiate with our People, Bias for Action, Continuous Improvement and Accountability define who we are and how we work. Join us!

POSITION SUMMARY

The Data Engineer is the hands-on builder and operator of AirLife’s Databricks Lakehouse, reporting to the Data Engineering & Platform Manager. This role builds, operates, and improves the pipelines that bring data from SAP ECC, Microsoft Dynamics GP, Salesforce, and other operational systems into the Lakehouse, and applies the governance, architecture, and cost standards the Manager defines. As AirLife’s analytics and AI platform matures, this role is the execution layer that keeps pipelines reliable, documented, and ready for business and AI consumption.

POSITION QUALIFICATIONS

Knowledge, Skills, & Abilities:

Hands-on, production experience building on Databricks: Delta Lake, Unity Catalog, Databricks Workflows / Lakeflow Jobs, and Lakeflow Declarative Pipelines.
Strong Python and PySpark for data engineering; advanced SQL.
Experience ingesting ERP and CRM data into a lakehouse; SAP ECC required or strongly preferred, Microsoft Dynamics GP and Salesforce a plus.
Working knowledge of Azure data services: Azure Data Lake Storage Gen2, Azure Data Factory, Azure SQL, and Azure Key Vault.
Software engineering practices applied to data: Git, pull requests and code review, CI/CD (Databricks Asset Bundles, Azure DevOps, or GitHub Actions), and automated data quality tests.
Practical understanding of Unity Catalog access controls, row- and column-level security, and data classification.
Experience implementing medallion architecture (bronze/silver/gold) and dimensional modeling for analytics consumption.
Working knowledge of data cataloging, lineage, and metadata management practices.
Awareness of data privacy and classification expectations in an FDA-regulated / ISO 13485 manufacturing environment.
Ability to read someone else's pipeline, figure out what it does and why, and take ownership of it.

Preferred:

Building data products for AI consumption: Databricks Genie / AI/BI, Databricks Apps, vector search, or serving curated data to LLM-based tools.
Experience with cost-aware engineering on Databricks - cluster and job right-sizing, serverless trade-offs, and reading system tables for usage.
Experience taking over work from consultants or offshore partners, running it in-house, and mentoring less-experienced engineers through the transition.
Exposure to Power BI as a downstream consumer of lakehouse data.
Infrastructure-as-code (Terraform) for Databricks/Azure resources.
dbt (data build tool) or equivalent transformation framework experience.

Level of Experience:

2+ years of hands-on Databricks pipeline development in an enterprise environment. Manufacturing, healthcare, or life sciences data experience preferred.

Level of Education:

Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field, or equivalent practical experience. Databricks Certified Data Engineer Associate or Professional preferred.

Travel:

Up to 10% as required by the business.

ESSENTIAL DUTIES AND RESPONSIBILITIES

Build, test, and deploy data pipelines - including medallion (bronze/silver/gold) layers - that ingest data from SAP ECC, Microsoft Dynamics GP, Salesforce, and other operational systems into the Databricks Lakehouse, using batch and incremental patterns and the architecture standards set by the Data Engineering & Platform Manager.
Take ownership of pipelines, notebooks, and data models built by external implementation partners: lead the technical side of knowledge transfer, document what exists, close gaps, and run it in production.
Operate what you build: monitor job health and data freshness, triage failures, fix root causes, and keep downstream teams informed when data is late or wrong.
Write data quality checks into pipelines (expectations, reconciliations to source, row-count and null-rate monitors) and act on what they find.
Apply Unity Catalog governance standards in daily work - naming, tagging, ownership, lineage documentation, metadata management, and access grants - as defined by the Data Engineering & Platform Manager.
Implement data security and access controls on the data you manage, consistent with AirLife's data classification standards and FDA 21 CFR Part 820 / ISO 13485 data-integrity expectations.
Participate in integration design reviews and new data source onboarding evaluations led by the Data Engineering & Platform Manager, and partner with Commercial Analytics, Supply Chain Analytics, Master Data, and the AI Lead to translate their requirements into pipeline and data model changes that support self-service BI and AI use cases.
Support integrations that write data back to operational systems; keep code in source control, work through pull requests, and deploy through CI/CD.
Write and maintain runbooks and technical documentation, including data lineage and pipeline architecture documentation.
Watch compute cost and performance on the jobs you own - right-size clusters and schedules, tune queries and pipelines (partitioning, caching), flag risks early, and provide usage data from Databricks system tables to support the Manager's cost governance.
Bring forward improvements to standards, patterns, and tooling.
Support the onboarding and technical mentoring of junior data engineers or offshore/outsourced engineering resources as the team grows.

OTHER RESPONSIBILITIES

Uphold and embody AirLife’s values in all aspects of work.
Demonstrate accuracy and thoroughness in daily work; look for ways to improve and promote quality & safety.
Inspire the trust of others; treat people with respect and dignity and embrace the value of diversity.
Use time efficiently; perform job accurately, thoroughly, and conserve Company resources to improve profits.
Contribute to building and maintaining a positive team environment.
Assure all policies and guidelines are implemented and followed.

QUALITY POLICY

At AirLife, Quality is our promise. It is our commitment to customer satisfaction and our dedication to product excellence in an evolving global healthcare market. This promise is kept through a continuously improving and effective Quality Management System and compliance to Regulatory Requirements.

DEIA STATEMENT

At AirLife, we are committed to building a diverse workforce and an inclusive workplace that reflects the communities and customers we serve. We believe our philosophy on Diversity, Equity, Inclusion, and Advancement (DEIA) encourages excellence and equips us to serve an evolving global marketplace.

BENEFITS

AirLife offers a comprehensive benefits package, including medical, dental, and vision coverage, 401(k), paid time off, paid holidays, bereavement leave, Employee Assistance Program resources, and medical leave benefits, subject to applicable eligibility requirements. Certain positions may also be eligible for bonus, commission, or other incentive compensation.

Please note: The responsibilities outlined above are not exhaustive and may evolve over time. The role holder may be required to undertake additional duties as reasonably expected to meet the needs of the company.

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More about this job

Skills

SAP
Microsoft Dynamics GP
Salesforce
Databricks
Unity
Python
PySpark
Data engineering
SQL
ERP
Microsoft Azure
Microsoft Azure Data Factory
Git
CI/CD
DevOps
GitHub
Cataloging
Coaching
Microsoft Power BI
Infrastructure as code
IBM Terraform
dbt
Computer science
Architecture regulations
Data models
Statistical quality control
Account reconciliation
Documentation
Apply information security policies
Onboarding
Google Analytics
Supply chain management
Customer service
Quality assurance procedures
Regulatory compliance

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