Note: The job is a remote job and is open to candidates in USA. Dynatron Software, Inc. is transforming the automotive service industry with intelligent SaaS solutions. They are seeking a highly skilled Senior Data Engineer to build, optimize, and maintain robust data pipelines that power real-time analytics and AI/ML initiatives.

Responsibilities

• Build and maintain complex data pipelines using AWS Glue, Step Functions, or Databricks Workflows
• Implement modular data structures using advanced modeling techniques such as Medallion Architecture and Dimensional Modeling
• Manage scalable data storage solutions using AWS S3 as the primary landing zone and data lake foundation
• Optimize storage formats (Delta, Iceberg, Parquet) and compute performance to ensure high-throughput and cost-effective processing
• Build decoupled, event-driven architectures using AWS SNS and SQS to handle high-throughput messaging between data services
• Develop and deploy real-time ingestion pipelines using AWS Kinesis or Kafka
• Implement Change Data Capture (CDC) via tools like Debezium or Fivetran to support low-latency operational analytics
• Own end-to-end data validation and QA by building automated data quality checks directly into the ETL/ELT pipelines
• Enforce strict data contracts and schema evolution guidelines to maintain high data quality and integrity across domains
• Implement proactive alerting and observability to catch data drift, pipeline anomalies, and quality drops before they impact downstream users
• Engineer ML-ready datasets and manage Feature Stores to support the Data Science team
• Operationalize ML workflows, integrating with services like Snowflake Cortex, Databricks AI, or AWS Bedrock
• Mentor junior engineers in coding best practices, SQL optimization, and Python development
• Collaborate closely with Product and ML teams to translate architectural designs into functional code

Skills

• 6–8+ years of experience in data engineering with a focus on large-scale distributed systems
• Expert-level Python and PySpark with Strong SQL skills
• Deep hands-on experience with Snowflake or Databricks, built natively within an AWS ecosystem
• Proven track record building streaming applications using Kinesis or Kafka
• Demonstrated experience implementing automated testing frameworks, data profiling, and pipeline validation (owning the QA of your own pipelines)
• Strong documentation habits (playbooks, technical specs) and an ownership mindset
• Strong communication skills with the ability to explain technical concepts clearly to technical and non-technical stakeholders
• Collaborative mindset with the ability to partner effectively across Product, Engineering, Analytics, ML, and leadership teams
• High standards for quality, maintainability, performance, and operational discipline
• Strong ownership mindset with the ability to move quickly, solve problems thoughtfully
• Relevant IT professional certifications, such as SnowPro Core, Databricks Certified Data Engineer Professional, or AWS Certified Data Engineer

Benefits

• Remote-first environment offering flexibility, autonomy, and trust.

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