Public Health Foundation Enterprises, In
Data Scientist - Analytics Engineer
Public Health Foundation Enterprises, In, Los Angeles, California, United States, 90079
Salary Range: $8,840.09-$11,912.82 monthly
SUMMARY
Housing for Health (HFH) is a program office within Community Programs, a division under the Los Angeles County Department of Health Services (DHS) for the County of Los Angeles. HFH was created to support people experiencing homelessness with complex clinical needs. We support people in obtaining housing, improving their health and thriving in their communities. HFH is a core component of Los Angeles County’s effort to respond to the homeless emergency. Where appropriate to the job function, a hybrid work schedule may be available, with employees working both remotely and from the office, as needed.
The Analytics Engineer plays a key role on the Community Programs Data Engineering team, building the semantic layer that supports performance tracking, evaluation, and policy guidance across initiatives such as Housing for Health and other countywide data integration efforts. This position translates analytic requirements into curated silver and gold data models using Databricks and supports integration of data from normalized backend systems.
This is a chance to architect a scalable data environment from the ground up in a mission-driven context. The engineering team plays a central role in the County’s data strategy, with opportunities for mentorship, innovation, and cross-sector impact.
Work is primarily remote within California and on Pacific Time. In-person meetings occur monthly for critical collaboration moments. Travel for these meetings is not reimbursed.
ESSENTIAL FUNCTIONS
Build and maintain semantic data models (silver and gold layers) in Spark/Databricks, primarily through ELT/ETL pipelines written in PySpark.
Understand and identify entity relationships among large collections of normalized backend tables to design accurate, denormalized, analyst-ready structures.
Contribute to schema and catalog design decisions, including naming conventions for static vs. live feeds and ad hoc data use cases. This includes creating and maintaining documentation that clarifies data model logic, table relationships, and mapping assumptions to support downstream users and internal knowledge transfer.
Collaborate with program and analytic teams to understand and translate both business rules used to define data fields as well as needs of analytic teams using semantic layer to produce reports and dashboards.
Collaborate with the Privacy Engineer to ensure analytic datasets align with RBAC policies, de-identification requirements, and data classification standards set for Departmental and Countywide use.
Contribute to, update, and maintain centralized code repositories used for data transformations.
Participate in Dev/Prod promotion workflows using GitHub, ensuring proper validation and configuration for CI/CD deployment.
Apply expectations and version control to standardize, test, and document pipelines.
Collaborate with Evaluation and Reporting teams to align models with use cases and downstream needs.
Optimize existing workflows by refactoring/modernizing code bases to improve the speed and resilience of data pipelines.
Participate in integration of external and internal sources using Azure services and contribute to scalable, secure data pipelines.
Support data modeling standards and technical documentation practices.
Assist in onboarding and training analysts to use the semantic layer effectively.
JOB QUALIFICATIONS
Option I: Two (2) years of experience applying advanced statistical analyses, including predictive analytics or data engineering, to produce actionable recommendations to support data-driven program, policy, and operational decision-making, at a level equivalent to the Los Angeles County class of Predictive Data Analyst.
Experience at the level of Predictive Data Analyst is defined as using machine learning techniques or data engineering practices to analyze or support analysis of complex data sets and find statistically significant, meaningful predictive patterns, relevant to program goals, that human intelligence could not identify on its own.
Option II: A Bachelor’s degree from an accredited college in a field of applied research such as Data Science, Machine Learning, Mathematics, Statistics, Business Analytics, Psychology, Computer Science, or Public Health that included 12 semester or 18 quarter units of coursework in data science, data engineering, predictive analytics, quantitative research methods, or statistical analysis -AND- Four (4) years of experience applying data engineering, machine learning, predictive analytics, and data management, to conduct or support hypothesis-driven data analysis to produce actionable recommendations to support data-driven program, policy, and operational decision-making.
A Master’s or Doctoral degree from an accredited college or university in a field of applied research such Data Science, Machine Learning, Mathematics, Statistics, Business Analytics, Psychology, Public Health, or similar related fields may substitute for up to two (2) years of experience.
Certificates/Licenses/Clearances
A valid California Class C Driver License or the ability to utilize an alternative method of transportation when needed to carry out job-related essential functions.
Successful clearing through the Live Scan and the Health Clearance process with the County of Los Angeles.
Other Skills, Knowledge, and Abilities
Experience building data transformations and models in Databricks or Spark-based environments.
Strong knowledge of Medallion Architecture and curated model development (e.g. data normalization as well as dimensional modeling with star/snowflake schemas)
Skilled in working with normalized datasets and applying entity resolution techniques to build clean, reliable analytic tables joined across systems (e.g., MDM-linked client records).
Familiarity with data quality best practices and methods for identifying contaminations in data pipelines.
Experience performing root cause analysis to determine sources of pipeline failures.
Experience using declarative syntax to manage and implement data transformations in such tools as Delta Live Tables, dbt, and Spark.
Proficient in SQL, Python, GitHub, and CI/CD workflows.
Familiarity with orchestration tools and concepts (Lakeflow, Airflow, Cron, etc)
Experience developing and maintaining Databricks notebooks used in orchestrated jobs, including environment-based configuration using YAML/JSON.
Understanding of HIPAA, FERPA, and governance in health and social service data.
Experience supporting dashboards (Power BI, Tableau) and ensuring downstream data usability.
Ability to work across technical and program teams and contribute to shared engineering practices.
PHYSICAL DEMANDS
Stand:
Frequently
Walk:
Frequently
Sit:
Frequently
Reach Outward:
Occasionally
Reach Above Shoulder:
Occasionally
Climb, Crawl, Kneel, Bend:
Occasionally
Lift / Carry:
Occasionally - Up to 15 lbs.
Push/Pull:
Occasionally - Up to 15 lbs.
See:
Constantly
Taste/ Smell:
Not Applicable
Not Applicable =
Not required for essential functions
Occasionally =
(0 - 2 hrs./day)
Frequently =
(2 - 5 hrs./day)
Constantly =
(5+ hrs./day)
WORK ENVIRONMENT
General Office Setting, Indoors Temperature Controlled
EEOC STATEMENT
It is the policy of Heluna Health to provide equal employment opportunities to all employees and applicants, without regard to age (40 and over), national origin or ancestry, race, color, religion, sex, gender, sexual orientation, pregnancy or perceived pregnancy, reproductive health decision making, physical or mental disability, medical condition (including cancer or a record or history of cancer), AIDS or HIV, genetic information or characteristics, veteran status or military service.
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights notice from the Department of Labor.
SUMMARY
Housing for Health (HFH) is a program office within Community Programs, a division under the Los Angeles County Department of Health Services (DHS) for the County of Los Angeles. HFH was created to support people experiencing homelessness with complex clinical needs. We support people in obtaining housing, improving their health and thriving in their communities. HFH is a core component of Los Angeles County’s effort to respond to the homeless emergency. Where appropriate to the job function, a hybrid work schedule may be available, with employees working both remotely and from the office, as needed.
The Analytics Engineer plays a key role on the Community Programs Data Engineering team, building the semantic layer that supports performance tracking, evaluation, and policy guidance across initiatives such as Housing for Health and other countywide data integration efforts. This position translates analytic requirements into curated silver and gold data models using Databricks and supports integration of data from normalized backend systems.
This is a chance to architect a scalable data environment from the ground up in a mission-driven context. The engineering team plays a central role in the County’s data strategy, with opportunities for mentorship, innovation, and cross-sector impact.
Work is primarily remote within California and on Pacific Time. In-person meetings occur monthly for critical collaboration moments. Travel for these meetings is not reimbursed.
ESSENTIAL FUNCTIONS
Build and maintain semantic data models (silver and gold layers) in Spark/Databricks, primarily through ELT/ETL pipelines written in PySpark.
Understand and identify entity relationships among large collections of normalized backend tables to design accurate, denormalized, analyst-ready structures.
Contribute to schema and catalog design decisions, including naming conventions for static vs. live feeds and ad hoc data use cases. This includes creating and maintaining documentation that clarifies data model logic, table relationships, and mapping assumptions to support downstream users and internal knowledge transfer.
Collaborate with program and analytic teams to understand and translate both business rules used to define data fields as well as needs of analytic teams using semantic layer to produce reports and dashboards.
Collaborate with the Privacy Engineer to ensure analytic datasets align with RBAC policies, de-identification requirements, and data classification standards set for Departmental and Countywide use.
Contribute to, update, and maintain centralized code repositories used for data transformations.
Participate in Dev/Prod promotion workflows using GitHub, ensuring proper validation and configuration for CI/CD deployment.
Apply expectations and version control to standardize, test, and document pipelines.
Collaborate with Evaluation and Reporting teams to align models with use cases and downstream needs.
Optimize existing workflows by refactoring/modernizing code bases to improve the speed and resilience of data pipelines.
Participate in integration of external and internal sources using Azure services and contribute to scalable, secure data pipelines.
Support data modeling standards and technical documentation practices.
Assist in onboarding and training analysts to use the semantic layer effectively.
JOB QUALIFICATIONS
Option I: Two (2) years of experience applying advanced statistical analyses, including predictive analytics or data engineering, to produce actionable recommendations to support data-driven program, policy, and operational decision-making, at a level equivalent to the Los Angeles County class of Predictive Data Analyst.
Experience at the level of Predictive Data Analyst is defined as using machine learning techniques or data engineering practices to analyze or support analysis of complex data sets and find statistically significant, meaningful predictive patterns, relevant to program goals, that human intelligence could not identify on its own.
Option II: A Bachelor’s degree from an accredited college in a field of applied research such as Data Science, Machine Learning, Mathematics, Statistics, Business Analytics, Psychology, Computer Science, or Public Health that included 12 semester or 18 quarter units of coursework in data science, data engineering, predictive analytics, quantitative research methods, or statistical analysis -AND- Four (4) years of experience applying data engineering, machine learning, predictive analytics, and data management, to conduct or support hypothesis-driven data analysis to produce actionable recommendations to support data-driven program, policy, and operational decision-making.
A Master’s or Doctoral degree from an accredited college or university in a field of applied research such Data Science, Machine Learning, Mathematics, Statistics, Business Analytics, Psychology, Public Health, or similar related fields may substitute for up to two (2) years of experience.
Certificates/Licenses/Clearances
A valid California Class C Driver License or the ability to utilize an alternative method of transportation when needed to carry out job-related essential functions.
Successful clearing through the Live Scan and the Health Clearance process with the County of Los Angeles.
Other Skills, Knowledge, and Abilities
Experience building data transformations and models in Databricks or Spark-based environments.
Strong knowledge of Medallion Architecture and curated model development (e.g. data normalization as well as dimensional modeling with star/snowflake schemas)
Skilled in working with normalized datasets and applying entity resolution techniques to build clean, reliable analytic tables joined across systems (e.g., MDM-linked client records).
Familiarity with data quality best practices and methods for identifying contaminations in data pipelines.
Experience performing root cause analysis to determine sources of pipeline failures.
Experience using declarative syntax to manage and implement data transformations in such tools as Delta Live Tables, dbt, and Spark.
Proficient in SQL, Python, GitHub, and CI/CD workflows.
Familiarity with orchestration tools and concepts (Lakeflow, Airflow, Cron, etc)
Experience developing and maintaining Databricks notebooks used in orchestrated jobs, including environment-based configuration using YAML/JSON.
Understanding of HIPAA, FERPA, and governance in health and social service data.
Experience supporting dashboards (Power BI, Tableau) and ensuring downstream data usability.
Ability to work across technical and program teams and contribute to shared engineering practices.
PHYSICAL DEMANDS
Stand:
Frequently
Walk:
Frequently
Sit:
Frequently
Reach Outward:
Occasionally
Reach Above Shoulder:
Occasionally
Climb, Crawl, Kneel, Bend:
Occasionally
Lift / Carry:
Occasionally - Up to 15 lbs.
Push/Pull:
Occasionally - Up to 15 lbs.
See:
Constantly
Taste/ Smell:
Not Applicable
Not Applicable =
Not required for essential functions
Occasionally =
(0 - 2 hrs./day)
Frequently =
(2 - 5 hrs./day)
Constantly =
(5+ hrs./day)
WORK ENVIRONMENT
General Office Setting, Indoors Temperature Controlled
EEOC STATEMENT
It is the policy of Heluna Health to provide equal employment opportunities to all employees and applicants, without regard to age (40 and over), national origin or ancestry, race, color, religion, sex, gender, sexual orientation, pregnancy or perceived pregnancy, reproductive health decision making, physical or mental disability, medical condition (including cancer or a record or history of cancer), AIDS or HIV, genetic information or characteristics, veteran status or military service.
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights notice from the Department of Labor.