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.
Company Overview