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Description of the Role

As an Associate Data Engineer at Vytalize Health, you will support the data engineering team by handling critical operational tasks, resolving support tickets, and conducting discovery work that enables our senior engineers to stay focused on building and scaling data platforms. You will work with healthcare data pipelines, learn production data systems, and contribute to improving data quality, reliability, and documentation.
This is an ideal role for someone early in their data engineering career or transitioning into data engineering from a related field. You will be mentored by experienced data engineers, gain hands-on experience with real healthcare data, and learn both classical data engineering practices and modern platforms like Databricks. Your contributions—from fixing bugs to documenting systems to investigating data quality issues—directly support the reliability of our clinical data infrastructure. You will learn to think about data quality metrics, testing, and validation as core responsibilities.

Primary Responsibilities

• 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

Required Qualifications

• 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

Strong Pluses

• Prior experience working with healthcare data, clinical data formats (FHIR, HL7, CCD), or claims data

• Familiarity with cloud data platforms (AWS, Databricks, Snowflake) or data warehousing

• Experience with dbt or other data transformation frameworks

• Knowledge of data quality tools, monitoring, or observability platforms

• Experience with orchestration tools (Airflow, Databricks Workflows) or workflow automation

• Background in healthcare, pharmaceutical, or other regulated industry

• Previous internship or project experience in data engineering or analytics

• Familiarity with value-based care concepts, clinical workflows, or healthcare operations

• Experience with API integration or data ingestion from external sources

• Previous exposure to Databricks, Apache Spark, or distributed computing

• Experience writing tests or developing QA processes for data pipelines

This job description is not designed to cover or contain a comprehensive listing of activities, duties, or responsibilities that are required of the employee. Other duties, responsibilities, and activities may change or be assigned at any time with or without notice.