About the role
Data Engineer Permanent Position / Direct Hire!
Collaborating with product, marketing, analytics, and ML teams to gather requirements, clarify use cases, and translate business objectives into scalable backend and data engineering solutions in an ad-tech and subscription commerce environment.
Designing and developing Java- and Scala-based backend services and microservices (including Spring Boot services) to support event processing, user segmentation, and personalization features used in production web and email experiences.
Leading the design and implementation of large-scale event pipelines that process millions of daily events from multiple systems (e.g., Segment) and integrate with downstream applications such as Iterable, Kustomer, and Amplitude for campaign targeting and customer support workflows.
Building and maintaining end-to-end ETL and ELT data pipelines in Databricks using Airflow and Scala to integrate subscription, billing, campaign, and review data, enabling retention dashboards and other analytics use cases.
Engineering data models and transformation logic to support marketing and growth analytics, including churn analysis, renewal rate tracking, and campaign effectiveness metrics such as CAC, payback period, and ROAS.
Developing GenAI-driven tools, including a Text-to-SQL system that converts natural language questions from non-technical users into SQL queries, enabling self-service analytics and faster decision-making by business teams.
Implementing and optimizing MRR-by-campaign pipelines and dashboards so that stakeholders can monitor performance daily and adjust budgets and campaign strategies based on near real-time financial and marketing data.
Designing and maintaining ETL workflows that deliver enriched user profiles and marketing attributes into tools such as Iterable, enabling precise audience segmentation, trigger-based campaigns, and win-back flows.
Building and operating high-throughput GraphQL aggregation layers (e.g., Apollo GraphQL-based services) to process millions of daily requests for personalization and real-time decisioning in email and marketing workflows.
Performing data validation, quality checks, and debugging across pipelines and services, including root cause analysis of data mismatches, performance bottlenecks, and production issues, and implementing corrective actions.
Optimizing application and database performance by monitoring query behavior, resource utilization, and latency; designing dashboards and alerts to track system health and service-level objectives.
Supporting migration and modernization initiatives, including converting services from EC2 to Kubernetes and designing frameworks for data migration from relational databases (such as PostgreSQL) to NoSQL data stores (such as DynamoDB) to improve scalability and reduce maintenance costs.
Writing automation scripts and tooling (for example, in Scala, Python, or shell scripts) to standardize deployments, simplify data operations, and reduce manual effort in monitoring and maintenance activities.
Using CI/CD tools such as Jenkins and build tools such as Gradle, along with infrastructure-as-code and configuration management practices, to support reliable and repeatable deployments of ETL jobs and backend services.
Documenting system architecture, data flows, pipelines, and operational runbooks; maintaining knowledge bases and technical documentation to enable team onboarding and consistent operational practices.
Mentoring new hires and collaborating with cross-functional teams to align on objectives, review technical designs, and drive continuous improvement in engineering standards and data quality practices.
Extending monitoring and automation techniques learned from prior roles to implement database monitoring, dashboarding, and automated test scripts for backend and data platforms.
Full time direct hire
Collaborating with product, marketing, analytics, and ML teams to gather requirements, clarify use cases, and translate business objectives into scalable backend and data engineering solutions in an ad-tech and subscription commerce environment.
Designing and developing Java- and Scala-based backend services and microservices (including Spring Boot services) to support event processing, user segmentation, and personalization features used in production web and email experiences.
Leading the design and implementation of large-scale event pipelines that process millions of daily events from multiple systems (e.g., Segment) and integrate with downstream applications such as Iterable, Kustomer, and Amplitude for campaign targeting and customer support workflows.
Building and maintaining end-to-end ETL and ELT data pipelines in Databricks using Airflow and Scala to integrate subscription, billing, campaign, and review data, enabling retention dashboards and other analytics use cases.
Engineering data models and transformation logic to support marketing and growth analytics, including churn analysis, renewal rate tracking, and campaign effectiveness metrics such as CAC, payback period, and ROAS.
Developing GenAI-driven tools, including a Text-to-SQL system that converts natural language questions from non-technical users into SQL queries, enabling self-service analytics and faster decision-making by business teams.
Implementing and optimizing MRR-by-campaign pipelines and dashboards so that stakeholders can monitor performance daily and adjust budgets and campaign strategies based on near real-time financial and marketing data.
Designing and maintaining ETL workflows that deliver enriched user profiles and marketing attributes into tools such as Iterable, enabling precise audience segmentation, trigger-based campaigns, and win-back flows.
Building and operating high-throughput GraphQL aggregation layers (e.g., Apollo GraphQL-based services) to process millions of daily requests for personalization and real-time decisioning in email and marketing workflows.
Performing data validation, quality checks, and debugging across pipelines and services, including root cause analysis of data mismatches, performance bottlenecks, and production issues, and implementing corrective actions.
Optimizing application and database performance by monitoring query behavior, resource utilization, and latency; designing dashboards and alerts to track system health and service-level objectives.
Supporting migration and modernization initiatives, including converting services from EC2 to Kubernetes and designing frameworks for data migration from relational databases (such as PostgreSQL) to NoSQL data stores (such as DynamoDB) to improve scalability and reduce maintenance costs.
Writing automation scripts and tooling (for example, in Scala, Python, or shell scripts) to standardize deployments, simplify data operations, and reduce manual effort in monitoring and maintenance activities.
Using CI/CD tools such as Jenkins and build tools such as Gradle, along with infrastructure-as-code and configuration management practices, to support reliable and repeatable deployments of ETL jobs and backend services.
Documenting system architecture, data flows, pipelines, and operational runbooks; maintaining knowledge bases and technical documentation to enable team onboarding and consistent operational practices.
Mentoring new hires and collaborating with cross-functional teams to align on objectives, review technical designs, and drive continuous improvement in engineering standards and data quality practices.
Extending monitoring and automation techniques learned from prior roles to implement database monitoring, dashboarding, and automated test scripts for backend and data platforms.
Full time direct hire
Requirements
REQUIREMENTS
Data Engineer
Ability to work in a project driven environment
Data Engineer
Ability to work in a project driven environment
SAR Tech LLC is an equal opportunity employer. Applicants must be legally authorised to work in the United States.