Healthy Together
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Senior Data Engineer Location:
Remote (preference for Mountain Time Zone) Type:
Full-time About The Role
We’re looking for an experienced Senior Data Engineer to design, build, and maintain the data backbone of our fast-growing GovTech & healthcare platform. You’ll own end-to-end data pipelines, third-party integrations, and database modeling to power analytics, reporting, and operational workflows. As a core contributor, you’ll collaborate with Product, Engineering, QA, and DevOps to ensure data is accurate, accessible, and compliant with HIPAA, FedRAMP, and SOC-2 requirements. Key Responsibilities
ETL/ELT Pipeline Development: Architect, develop, and operate scalable data pipelines in Python using frameworks such as Apache Airflow, AWS Glue, or similar. ETL/ELT Pipeline Development details:
Ingest and transform data from internal sources, microservices, and third-party APIs (REST, streaming, webhooks).
Data Modeling & Warehousing: Design and maintain dimensional and normalized schemas in cloud data warehouses (AWS Redshift, Snowflake, or equivalent). Data Modeling & Warehousing details:
Optimize table structures, partitioning, and indexing for performance and cost efficiency.
Third-Party Integrations: Build and manage robust, fault-tolerant integrations with external systems (payment gateways, identity providers, data vendors). Third-Party Integrations details:
Develop monitoring, retries, and alerting to ensure integration reliability.
Data Quality & Governance: Implement data validation, anomaly detection, and reconciliation processes to guarantee accuracy. Data Quality & Governance details:
Collaborate with Security and Compliance teams to enforce data governance, encryption, and access controls.
Collaboration & Enablement: Partner with Analytics, ML, and Product teams to translate requirements into data solutions. Collaboration & Enablement details:
Provide self-service data access (views, dashboards) and documentation for stakeholders.
Performance & Cost Optimization: Monitor and tune pipeline and warehouse performance; identify opportunities to reduce AWS spend. Performance & Cost Optimization details:
Introduce caching, batching, and parallelism as appropriate for large-scale workloads.
Innovation & Tooling: Evaluate and prototype emerging data technologies (Spark, Kafka, dbt, data mesh patterns). Innovation & Tooling details:
Leverage AI/ML tools to automate repetitive data tasks or anomaly detection.
Required Qualifications
Experience: 7+ years in data engineering or analytics engineering roles. Core Language: Expert-level Python for ETL scripting, API clients, and automation. Cloud Proficiency: Hands-on with AWS data services (S3, Glue, Redshift, EMR, Lambda) and infrastructure-as-code (Terraform or CloudFormation). SQL & Data Modeling: Deep expertise designing relational schemas, writing complex SQL, and building data marts. Pipeline Frameworks: Proven experience with Apache Airflow, AWS Glue, or equivalent orchestration tools. Integration Skills: Solid background integrating and transforming data from third-party APIs, streaming platforms, and message queues. Regulatory Compliance: Familiarity with data handling requirements in HIPAA, FedRAMP, and SOC-2 environments. Collaboration: Strong communication skills; able to partner effectively with cross-functional teams. Preferred Qualifications
Big Data Technologies: Experience with Apache Spark, Kafka, Kinesis, or similar. Modern Transform Tools: Proficiency with dbt, Delta Lake, or Iceberg for versioned tables and transformations. Containerization & Orchestration: Knowledge of Docker and Kubernetes for data workloads. Machine Learning Pipelines: Exposure to MLOps frameworks and feature stores. Healthcare & Government Domain: Prior work on healthcare analytics or government data projects. Open-Source Contributions: Engagement with data-engineering or analytics OSS communities. Core Competencies & Expectations
Hands-On Contributor: You dive into code and configurations, continuously shipping reliable data solutions. Data Quality Champion: You obsess over accuracy, completeness, and timeliness of data. Problem Solver: You break down complex data challenges into clear designs and implementations. Collaborative Mindset: You communicate clearly, document thoroughly, and empower others with data. Continuous Learner: You stay current with evolving data architectures and share insights with the team. What We Offer
Competitive salary and equity packages Comprehensive health, dental, and vision benefits Monthly Wellness Stipend Generous PTO
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Senior Data Engineer Location:
Remote (preference for Mountain Time Zone) Type:
Full-time About The Role
We’re looking for an experienced Senior Data Engineer to design, build, and maintain the data backbone of our fast-growing GovTech & healthcare platform. You’ll own end-to-end data pipelines, third-party integrations, and database modeling to power analytics, reporting, and operational workflows. As a core contributor, you’ll collaborate with Product, Engineering, QA, and DevOps to ensure data is accurate, accessible, and compliant with HIPAA, FedRAMP, and SOC-2 requirements. Key Responsibilities
ETL/ELT Pipeline Development: Architect, develop, and operate scalable data pipelines in Python using frameworks such as Apache Airflow, AWS Glue, or similar. ETL/ELT Pipeline Development details:
Ingest and transform data from internal sources, microservices, and third-party APIs (REST, streaming, webhooks).
Data Modeling & Warehousing: Design and maintain dimensional and normalized schemas in cloud data warehouses (AWS Redshift, Snowflake, or equivalent). Data Modeling & Warehousing details:
Optimize table structures, partitioning, and indexing for performance and cost efficiency.
Third-Party Integrations: Build and manage robust, fault-tolerant integrations with external systems (payment gateways, identity providers, data vendors). Third-Party Integrations details:
Develop monitoring, retries, and alerting to ensure integration reliability.
Data Quality & Governance: Implement data validation, anomaly detection, and reconciliation processes to guarantee accuracy. Data Quality & Governance details:
Collaborate with Security and Compliance teams to enforce data governance, encryption, and access controls.
Collaboration & Enablement: Partner with Analytics, ML, and Product teams to translate requirements into data solutions. Collaboration & Enablement details:
Provide self-service data access (views, dashboards) and documentation for stakeholders.
Performance & Cost Optimization: Monitor and tune pipeline and warehouse performance; identify opportunities to reduce AWS spend. Performance & Cost Optimization details:
Introduce caching, batching, and parallelism as appropriate for large-scale workloads.
Innovation & Tooling: Evaluate and prototype emerging data technologies (Spark, Kafka, dbt, data mesh patterns). Innovation & Tooling details:
Leverage AI/ML tools to automate repetitive data tasks or anomaly detection.
Required Qualifications
Experience: 7+ years in data engineering or analytics engineering roles. Core Language: Expert-level Python for ETL scripting, API clients, and automation. Cloud Proficiency: Hands-on with AWS data services (S3, Glue, Redshift, EMR, Lambda) and infrastructure-as-code (Terraform or CloudFormation). SQL & Data Modeling: Deep expertise designing relational schemas, writing complex SQL, and building data marts. Pipeline Frameworks: Proven experience with Apache Airflow, AWS Glue, or equivalent orchestration tools. Integration Skills: Solid background integrating and transforming data from third-party APIs, streaming platforms, and message queues. Regulatory Compliance: Familiarity with data handling requirements in HIPAA, FedRAMP, and SOC-2 environments. Collaboration: Strong communication skills; able to partner effectively with cross-functional teams. Preferred Qualifications
Big Data Technologies: Experience with Apache Spark, Kafka, Kinesis, or similar. Modern Transform Tools: Proficiency with dbt, Delta Lake, or Iceberg for versioned tables and transformations. Containerization & Orchestration: Knowledge of Docker and Kubernetes for data workloads. Machine Learning Pipelines: Exposure to MLOps frameworks and feature stores. Healthcare & Government Domain: Prior work on healthcare analytics or government data projects. Open-Source Contributions: Engagement with data-engineering or analytics OSS communities. Core Competencies & Expectations
Hands-On Contributor: You dive into code and configurations, continuously shipping reliable data solutions. Data Quality Champion: You obsess over accuracy, completeness, and timeliness of data. Problem Solver: You break down complex data challenges into clear designs and implementations. Collaborative Mindset: You communicate clearly, document thoroughly, and empower others with data. Continuous Learner: You stay current with evolving data architectures and share insights with the team. What We Offer
Competitive salary and equity packages Comprehensive health, dental, and vision benefits Monthly Wellness Stipend Generous PTO
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