Senior IT Data Engineer

Mosaic

$103K — $154K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's degree required; 4-6 years of data engineering experience.
  • Advanced SQL proficiency and strong Python skills for data pipeline development.
  • Deep experience with data pipeline tools (e.g., dbt, Azure Data Factory, Airflow, Informatica) and cloud data platforms.
  • Experience with data modeling including dimensional design, data vault, or medallion architecture patterns.
  • Demonstrated ability to manage data engineering projects independently.
  • Professional-level cloud or data engineering certification preferred.

Responsibilities

  • Design and build scalable data pipelines for analytics, clinical, and operational consumers.
  • Lead data engineering workstreams from requirements to production deployment.
  • Develop and optimize SQL, Python, and pipeline code for performance and maintainability.
  • Contribute to decisions on data platform architecture, including storage design and processing patterns.
  • Identify data quality issues and drive resolution with source system owners.
  • Establish engineering standards for pipeline development, testing, and documentation.
  • Mentor junior data engineers and support team capability development.

Benefits

  • Flexible working hours and remote work options.
  • Opportunity for professional development and certifications.
  • Collaborative team environment with strong mentorship opportunities.
  • Access to cutting-edge tools and technologies for data engineering.
Full Job Description
Job Description Summary

Designs and develops enterprise data pipelines, data models, and platform integrations that support analytics, clinical, and operational use cases at the organization. Leads data engineering workstreams independently, contributes to platform architecture, and serves as a technical resource on complex data challenges.

The Senior IT Data Engineer works independently on enterprise-impact projects with limited supervision. Decisions impact project- and program-level outcomes. This job sets direction on methods and approach within the IT data engineering domain and influences policies and procedures within the function, applying advanced subject-matter expertise to problems that span teams and processes.

How will you make an impact & Requirements

KEY RESPONSIBILITIES
  • Design and build scalable data pipelines, ETL/ELT processes, and data models that serve analytics, clinical, and operational consumers.
  • Lead data engineering workstreams from requirements through production deployment, managing technical quality and delivery milestones.
  • Develop and optimize SQL, Python, and pipeline code to ensure performance, reliability, and maintainability at scale.
  • Contribute to data platform architecture decisions including storage design, processing patterns, and tooling selection.
  • Identify data quality issues and partner with source system owners to drive resolution and preventive improvements.
  • Establish engineering standards for pipeline development, testing, documentation, and version control.
  • Mentor junior data engineers and contribute to capability development across the team.


QUALIFICATIONS
  • Bachelor's degree required; 4-6 years of data engineering experience.
  • Advanced SQL proficiency and strong Python skills for data pipeline development.
  • Deep experience with data pipeline tools (e.g., dbt, Azure Data Factory, Airflow, Informatica) and cloud data platforms.
  • Experience with data modeling including dimensional design, data vault, or medallion architecture patterns.
  • Demonstrated ability to manage data engineering projects independently.
  • Professional-level cloud or data engineering certification preferred.


Compensation Range:
$103,043.00
to
$154,563.00

The anticipated base salary range represents the Company's good-faith estimate of the compensation it reasonably expects to pay for this position at the time of posting. Actual compensation will be determined based on factors including experience, skills, qualifications, geographic location, internal equity, and business needs.

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