H
AI Summary
Design, build, and maintain scalable ETL/ELT pipelines for batch and streaming data. Collaborate with Data Scientists and Analysts to translate requirements into performant data solutions. Troubleshoot production incidents and tune pipeline performance.
Key Highlights
Design and maintain scalable ETL/ELT pipelines
Collaborate with Data Scientists and Analysts
Troubleshoot production incidents and tune pipeline performance
Technical Skills Required
Benefits & Perks
Fully remote role
Flexible work hours
Competitive compensation
Learning and development support
Modern cloud-first tech stack
Job Description
About The Opportunity
A high-growth player in the Data Engineering & Analytics sector, we build scalable, secure data infrastructure and analytics platforms that power business intelligence and operational analytics for enterprise customers. We deliver production-grade ETL/ELT pipelines, data warehouses, and streaming systems to support data-driven decision making across the organization.
Primary Title: Data Engineer (Remote, USA)
Role & Responsibilities
- Design, build and maintain scalable ETL/ELT pipelines for batch and streaming data to support analytics and ML use-cases.
- Author and optimize SQL and Python-based data processing jobs using Spark and cloud-native services to ensure reliability and cost-efficiency.
- Develop and operate orchestration workflows (Airflow) and CI/CD for data deployments, monitoring, and automated recovery.
- Implement and enforce data modelling, partitioning, and governance best practices across data lakes and warehouses (Snowflake/Databricks).
- Collaborate with Data Scientists, Analysts, and Product teams to translate requirements into performant data solutions and delivery timelines.
- Troubleshoot production incidents, tune pipeline performance, and document operational runbooks and observability metrics.
Must-Have
- Proficiency in Python for data engineering and automation tasks.
- Strong SQL skills for analytics, ETL validation, and performance tuning.
- Hands-on experience with Apache Spark for large-scale data processing.
- Experience building and scheduling workflows with Apache Airflow (or equivalent).
- Familiarity with cloud data platforms and services (AWS preferred) and Snowflake.
- Proven experience designing production-grade data pipelines and implementing data quality/observability.
- Experience with Databricks for collaborative Spark workloads.
- Knowledge of streaming platforms such as Apache Kafka.
- Infrastructure-as-code experience (Terraform) and containerization (Docker).
- Fully remote role with flexible work hours to support work–life balance.
- Opportunities for career growth, cross-functional collaboration, and technical mentorship.
- Competitive compensation, learning and development support, and modern cloud-first tech stack.
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