Data Engineer

DataSpring

17d•$100,000 - $115,000•Remote

About The Position

The Data Engineer will support the design, development, and maintenance of data pipelines and data models that power DataSpring's analytics and data platforms. This role focuses on implementing scalable data solutions, ensuring data quality, and collaborating with senior engineers and stakeholders to deliver reliable data for reporting and analysis. This position provides an opportunity to build hands-on experience with Databricks, Azure SQL, and modern data engineering practices while contributing to enterprise data initiatives and discussions.

Requirements

Strong foundational SQL skills (complex joins, reconciliation, performance tuning).
Familiarity with Databricks, Delta Lake, and Azure SQL.
Basic understanding of data modeling for analytical, operational, and API‑driven use cases.
Ability to support troubleshooting of messy, evolving enterprise data domains.
Excellent written and verbal communication, especially for explaining complex data behavior to non‑technical stakeholders.
Experience using Git, DevOps tools, and CI/CD pipelines for data engineering workflows.
1–3 years of experience in a data engineering or analytics engineering role, including internships or academic projects.
Demonstrated success contributing to data modernization or migration initiatives in cloud environments.
Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related field.

Nice To Haves

Prior experience working with healthcare or other regulated data environments is highly desirable.
Azure Data Engineer Associate or related certification (preferred).
Coursework or certification in AI/ML (preferred but not required).

Responsibilities

Build and maintain ETL/ELT pipelines across Databricks, Azure SQL, and downstream gold-layer models supporting priority projects
Support development and enhancement of enriched data models, including field-level enrichment logic, recency rules, and provider-level enrichment flags.
Assist in maintaining data logic, including reconciliation between source and target data sources and resolution of duplication and data discrepancies.
Assist in implementing medallion architecture patterns (bronze → gold), ensuring data quality, traceability, and performance at scale.
Support identification and resolution of systemic data quality issues, including null handling, soft deletes, authorization flags, and incorrect organizational mappings.
Support implementation of rules for data in collaboration with product, governance, and engineering stakeholders.
Assist in documenting (Confluence, mapping workbooks) to serve as a single source of truth for enrichment logic and data behavior.
Support collaboration with vendors and partners for vendors providing detailed queries, validation logic, and corrective guidance on upstream data issues.
Collaborate with product owners and engineering teams to ensure data models align with product defined use cases.
Support UAT and release readiness by preparing data, validating counts, and resolving last‑mile data defects under tight timelines.