Senior Data Engineer (AWS)

TL;DR

Senior Data Engineer (AWS/Snowflake): Building and optimizing robust data pipelines to power real-time analytics, AI/ML initiatives, and enterprise reporting with an accent on AWS data lake engineering and real-time ingestion. Focus on implementing Medallion Architecture, ensuring data quality through automated validation, and operationalizing ML workflows.

Location: Remote (Required availability during U.S. core collaboration hours: 9:00 AM – 2:00 PM EST)

Salary: $45,000 USD Annually

Company

AI-enabled SaaS company transforming the automotive service industry with intelligent analytics and automation.

What you will do

• Develop complex data pipelines using AWS Glue, Step Functions, or Databricks Workflows.
• Implement modular data structures using Medallion Architecture and Dimensional Modeling on AWS S3.
• Build real-time ingestion pipelines using AWS Kinesis or Kafka and implement CDC via Debezium or Fivetran.
• Own end-to-end data validation by building automated quality checks and proactive alerting into ETL/ELT pipelines.
• Engineer ML-ready datasets and Feature Stores, integrating with Snowflake Cortex, Databricks AI, or AWS Bedrock.
• Mentor junior engineers in coding best practices, SQL optimization, and Python development.

Requirements

• 6–8+ years of experience in data engineering with a focus on large-scale distributed systems.
• Expert-level proficiency in Python, PySpark, and Strong SQL.
• Deep hands-on experience with Snowflake or Databricks natively within the AWS ecosystem.
• Proven track record of building streaming applications using Kinesis or Kafka.
• Experience implementing automated testing frameworks, data profiling, and pipeline validation.
• Must be available during U.S. core collaboration hours (9 AM – 2 PM EST).

Nice to have

• Relevant certifications such as SnowPro Core, Databricks Certified Data Engineer Professional, or AWS Certified Data Engineer.

Culture & Benefits

• Remote-first environment offering flexibility, autonomy, and trust.
• High-impact role supporting real-time analytics and product innovation.
• Values-driven culture focused on accountability, urgency, and measurable results.
• Close partnership across Data, Product, Engineering, Analytics, and business leadership.