Jobot
Overview
This role is an urgent opening for a Senior Data Engineer. Base pay range is $80,000.00/yr - $130,000.00/yr. Salary is based on skills and experience.
Job details and company information are provided by Jobot and will be discussed with the recruiter.
Base pay range $80,000.00/yr - $130,000.00/yr
Why join us We are building a nationally recognized cannabis brand with a focus on exceptional customer service and a diversified product lineup. We value Influence, Inspire, Innovate, Win, and Grow, and seek to expand our growth across the United States and beyond.
Benefits & Compensation Details about compensation and benefits eligibility are provided during the hiring process. We offer competitive compensation, paid training, and employee discounts on our products and services. Benefit packages vary by eligibility and may include:
Paid Vacation Time, Paid Sick Leave, Paid Holidays, Parental Leave
Health, Dental, and Vision Insurance
Employee Assistance Program
401k with employer match
Life Insurance
What you’ll do
Own the lakehouse. Design and evolve Data Lake/Lakehouse (Delta Lake) on AWS and/or Azure, from storage layout and security to compute strategy (Databricks, EMR, Synapse) and SLAs.
Build pipelines. Develop batch and streaming ETL/ELT with Apache Spark (PySpark/Scala), Databricks Workflows, and/or Azure Data Factory; integrate sources via APIs, CDC, and message buses (Kinesis/Event Hubs/Kafka).
Model for analytics & ML. Deliver well-tested models and dimensional/semantic layers for Databricks SQL powering BI and production ML features.
Engineer for production. Implement observability, CI/CD, infrastructure as code, and automated testing.
Optimize performance and cost. Right-size clusters, tune Spark jobs, partitioning, caching, and storage formats to improve runtime and reduce cloud spend.
Harden governance. Enforce RBAC/row-level security, cataloging/lineage, PII handling, and compliance (GDPR; HIPAA/FHIR; CCPA/CDPA experience is a plus).
Partner cross-functionally. Collaborate with analytics, product, and platform teams to define SLAs, incident response/runbooks, and data contract best practices.
Mentor and lead. Drive design reviews, coach engineers, and set engineering standards for reliability and maintainability.
What you’ll bring
8+ years of hands-on data engineering experience (flexible for exceptional builders).
Expertise in Python and SQL; working knowledge of Scala/Java for Spark when needed.
3–5+ years with Apache Spark and Databricks (jobs, clusters, Delta Live Tables, performance tuning).
Strong cloud experience in AWS (S3, EMR, Glue, Lambda, Kinesis, IAM) and/or Azure (ADLS Gen2, ADF, Event Hubs, Synapse, Functions).
Production orchestration with Airflow and/or ADF; dbt for transformation.
Warehouse experience with Snowflake (preferred) and/or Redshift/Databricks SQL.
Proven cost/performance optimization and reliability improvements.
Solid grounding in data modeling (dimensional/star), testing, CI/CD, Docker/Linux, and Git.
Ability to translate business needs into scalable data products and lead projects end-to-end.
Nice to have
Experience building custom Spark listeners/metrics, event-driven architectures, or low-latency streaming.
FHIR/healthcare data pipelines and DLT troubleshooting.
Power BI/Tableau/Looker dashboarding to close the loop on quality and impact.
Exposure to governance frameworks (catalog/lineage, RLS, data contracts) and MLOps (feature stores, model monitoring).
Job details Job is hosted by Jobot. Equal Opportunity Employer. Details about rights and protections in the workplace are provided by Jobot and are in compliance with applicable laws.
Qualifications Senior-level role requiring substantial data engineering expertise and leadership potential. Not Applicable for seniority labeling in some postings, but role aligns with Senior Data Engineer responsibilities described above.
#J-18808-Ljbffr
Job details and company information are provided by Jobot and will be discussed with the recruiter.
Base pay range $80,000.00/yr - $130,000.00/yr
Why join us We are building a nationally recognized cannabis brand with a focus on exceptional customer service and a diversified product lineup. We value Influence, Inspire, Innovate, Win, and Grow, and seek to expand our growth across the United States and beyond.
Benefits & Compensation Details about compensation and benefits eligibility are provided during the hiring process. We offer competitive compensation, paid training, and employee discounts on our products and services. Benefit packages vary by eligibility and may include:
Paid Vacation Time, Paid Sick Leave, Paid Holidays, Parental Leave
Health, Dental, and Vision Insurance
Employee Assistance Program
401k with employer match
Life Insurance
What you’ll do
Own the lakehouse. Design and evolve Data Lake/Lakehouse (Delta Lake) on AWS and/or Azure, from storage layout and security to compute strategy (Databricks, EMR, Synapse) and SLAs.
Build pipelines. Develop batch and streaming ETL/ELT with Apache Spark (PySpark/Scala), Databricks Workflows, and/or Azure Data Factory; integrate sources via APIs, CDC, and message buses (Kinesis/Event Hubs/Kafka).
Model for analytics & ML. Deliver well-tested models and dimensional/semantic layers for Databricks SQL powering BI and production ML features.
Engineer for production. Implement observability, CI/CD, infrastructure as code, and automated testing.
Optimize performance and cost. Right-size clusters, tune Spark jobs, partitioning, caching, and storage formats to improve runtime and reduce cloud spend.
Harden governance. Enforce RBAC/row-level security, cataloging/lineage, PII handling, and compliance (GDPR; HIPAA/FHIR; CCPA/CDPA experience is a plus).
Partner cross-functionally. Collaborate with analytics, product, and platform teams to define SLAs, incident response/runbooks, and data contract best practices.
Mentor and lead. Drive design reviews, coach engineers, and set engineering standards for reliability and maintainability.
What you’ll bring
8+ years of hands-on data engineering experience (flexible for exceptional builders).
Expertise in Python and SQL; working knowledge of Scala/Java for Spark when needed.
3–5+ years with Apache Spark and Databricks (jobs, clusters, Delta Live Tables, performance tuning).
Strong cloud experience in AWS (S3, EMR, Glue, Lambda, Kinesis, IAM) and/or Azure (ADLS Gen2, ADF, Event Hubs, Synapse, Functions).
Production orchestration with Airflow and/or ADF; dbt for transformation.
Warehouse experience with Snowflake (preferred) and/or Redshift/Databricks SQL.
Proven cost/performance optimization and reliability improvements.
Solid grounding in data modeling (dimensional/star), testing, CI/CD, Docker/Linux, and Git.
Ability to translate business needs into scalable data products and lead projects end-to-end.
Nice to have
Experience building custom Spark listeners/metrics, event-driven architectures, or low-latency streaming.
FHIR/healthcare data pipelines and DLT troubleshooting.
Power BI/Tableau/Looker dashboarding to close the loop on quality and impact.
Exposure to governance frameworks (catalog/lineage, RLS, data contracts) and MLOps (feature stores, model monitoring).
Job details Job is hosted by Jobot. Equal Opportunity Employer. Details about rights and protections in the workplace are provided by Jobot and are in compliance with applicable laws.
Qualifications Senior-level role requiring substantial data engineering expertise and leadership potential. Not Applicable for seniority labeling in some postings, but role aligns with Senior Data Engineer responsibilities described above.
#J-18808-Ljbffr