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Data Engineer
Vytalize HealthAssociate Data Engineer supporting healthcare data engineering and improving data quality at Vytalize Health. Contributing to operations and working with senior engineers on data platforms.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Proficient in SQL and Python for data validation, analysis, and automation, with a solid understanding of data quality concepts and ETL/ELT processes. Demonstrates strong analytical and problem-solving skills, attention to detail, and effective communication for documenting findings and collaborating with senior engineers.
Highest-signal resume keywords
SQL ProficiencyPython ProgrammingData Quality AssuranceData Pipeline ArchitectureVersion Control (Git)
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
SQLPythonData ValidationETL/ELT ConceptsData ModelingData AnalysisDebuggingData Quality MetricsData ProfilingData Documentation
Soft Skills
Strong Communication SkillsAnalytical MindsetProblem-Solving SkillsAttention to DetailWillingness to Learn
Tools & Technologies
GitData PipelinesTesting FrameworksMonitoring ToolsData Transformation Tools
Industry Keywords
Data QualityData EngineeringOperational SupportKnowledge SharingData Anomalies
Tech Stack
Tools & technologiesETLPythonSQL
About the role
Key responsibilities & impact- Handle support tickets and operational issues reported by internal teams and external partners; investigate root causes and coordinate resolution with senior engineers
- Perform KTLO (Keep The Lights On) tasks including monitoring pipeline health, responding to alerts, validating data quality, and investigating data anomalies
- Conduct data source discovery and profiling work — examining raw data sources, documenting data structure, identifying quality issues, and recommending integration approaches
- Assist with data validation and testing — writing SQL queries to validate data transformations, identifying gaps and inconsistencies, and flagging issues for review
- Support data quality initiatives by running diagnostics, documenting data quality findings, and escalating issues with clear context for senior engineers
- Assist in establishing and monitoring data quality metrics — working with senior engineers to define quality KPIs and track pipeline health
- Help maintain and improve documentation for existing data systems, pipelines, and data sources — documenting schemas, transformation logic, and known issues
- Assist senior engineers with debugging data pipeline issues — tracing data through transformations, validating intermediate outputs, and comparing expected vs. actual results
- Conduct quality assurance activities — reviewing data outputs, testing transformations, and validating correctness before data reaches downstream consumers
- Perform exploratory data analysis to understand data patterns, support analytics requests, and help answer business questions about data availability and quality
- Learn and apply data engineering best practices including version control (Git), code review processes, and testing frameworks under guidance from senior engineers
- Support infrastructure and operational tasks as assigned — assisting with deployments, maintaining environments, and supporting on-call activities
- Participate in knowledge-sharing and mentorship; ask questions, document learnings, and contribute to team documentation and runbooks
Requirements
What you’ll need- Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent hands-on experience
- Strong SQL proficiency — ability to write queries to explore, validate, and analyze data
- Proficiency in Python or another programming language; comfort writing scripts and automation
- Basic understanding of data modeling, ETL/ELT concepts, and data pipeline architecture
- Familiarity with version control (Git) and collaborative development practices
- Strong communication skills; ability to document findings clearly and ask clarifying questions
- Analytical mindset and strong problem-solving skills, especially for data quality and debugging tasks
- Attention to detail and commitment to data accuracy and reliability
- Basic understanding of data quality concepts and the importance of testing and validation
- Willingness to learn from experienced engineers and grow into a full data engineer role
Benefits
Comp & perks- Health insurance
- Professional development opportunities