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Data Engineer
Job Description
The Level 7 Data Engineer is responsible for designing, developing, and supporting business-critical real-time streaming data pipelines for event processing. Operating with a high level of independence, this role delivers scalable, resilient, and secure cloud-based data solutions that directly impact digital revenue and customer experience. The position collaborates closely with platform, analytics, and engineering teams in a fully remote environment.
Key Responsibilities
•Design, build, and maintain real-time streaming pipelines processing high-volume event data using Python, PySpark, Snowflake, and AWS.
•Develop scalable ingestion and transformation workflows leveraging Airflow (Astronomer), Informatica, and dbt (Core/Cloud).
•Optimize data models and warehouse structures in Snowflake to support low-latency analytics and operational reporting.
•Ensure reliability, scalability, and fault tolerance of business-critical streaming workflows.
•Implement CI/CD best practices using GitLab and automate testing, deployment, and monitoring processes.
•Partner with cross-functional stakeholders to translate real-time digital commerce requirements into robust data engineering solutions.
•Proactively monitor production pipelines, troubleshoot incidents, and resolve performance bottlenecks with minimal supervision.
•Participate in on-call rotations to provide 24/7 support for critical (P1/P2) incidents affecting production systems
•Enforce data governance, security controls, and data quality standards across ingestion and transformation layers.
•Leverage AI-enabled development tools and remain current on emerging AI trends to improve automation, documentation, code efficiency, and operational productivity.
Required Qualifications
•4+ years of experience in data engineering with strong hands-on expertise in Python, SQL, PySpark, Snowflake, and AWS services like SQS, Kinesis, Eventbridge, S3, Lambda, etc
•Proven experience building and supporting real-time or near real-time streaming data pipelines in production environments.
•Solid understanding of data modeling, ETL/ELT design patterns, CI/CD practices, and cloud-native architecture.
•Experience with Airflow (Astronomer), dbt, Informatica, and GitLab-based deployment workflows.
•Demonstrated ability to independently manage moderately complex initiatives supporting business-critical systems in a remote environment.
Preferred Qualifications
•Experience working with eCommerce event-driven architectures and digital transaction ecosystems.
•Experience implementing data observability, monitoring, and automated data quality frameworks.
•Demonstrated application of AI tools to enhance engineering efficiency, workflow automation, and solution delivery.
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CEO, LanceSoft
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