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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Dynatron Corporation - **Location:** Atlanta, GA, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Artificial Intelligence, Amazon Web Services, Amazon S3, Data Analysis, ARM Architecture, Data as a Services, Data Validation, Information Engineering, Extract Transform Load (ETL), Data Profiling, Dataspaces, Data Structures, Database Queries, Dimensional Modeling, Distributed Systems, Python (Programming Language), Machine Learning, Data Streaming, Parquet, Data Ingestion, Sql Optimization, Snowflake, Change Data Capture, Event Driven Architecture, Data Lakes, Pyspark, Core Data, Debezium, AWS Glue, Apache Kafka, Machine Learning Operations, Amazon Simple Queue Service (SQS), Stream Processing, Data Pipelines, Databricks - **Published:** August 12, 2026 - **Apply:** https://www.workingnomads.com/job/go/1787778/ ## About the Role * Experience: 6-8+ years of experience in data engineering with a focus on large-scale distributed systems. * Core Languages: Expert-level Python and PySpark with Strong SQL skills. * Platforms: Deep hands-on experience with Snowflake or Databricks, built natively within an AWS ecosystem. * Streaming: Proven track record building streaming applications using Kinesis or Kafka. * Data Validation: Demonstrated experience implementing automated testing frameworks, data profiling, and pipeline validation (owning the QA of your own pipelines). * Soft Skills: Strong documentation habits (playbooks, technical specs) and an ownership mindset. * Certifications (Nice-to-Have): Relevant IT professional certifications, such as SnowPro Core, Databricks Certified Data Engineer Professional, or AWS Certified Data Engineer. Collaboration & Ownership * 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 ## Description Dynatron is seeking a highly skilled Senior Data Engineer to join our growing data team. While our architects define the blueprint, you will be the lead craftsman responsible for building, optimizing, and maintaining the robust data pipelines that power our real-time analytics, AI/ML initiatives, and enterprise reporting. You are a hands-on expert in AWS and modern cloud data stacks, specifically Snowflake or Databricks, and possess the engineering rigor to build scalable, production-grade data ecosystems., * 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. Real-Time Data Streaming & Ingestion * 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. Core Data Quality & Automated Validation (QA Ownership) * 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. Engineering for ML/AI * 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. Technical Leadership & Collaboration * 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. ## Related Videos - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Parquet, Delta, Iceberg & Ducklake - An introduction for developers](https://www.wearedevelopers.com/videos/100075-parquet-delta-iceberg-ducklake-an-introduction-for-developers) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [How we built an AI-powered code reviewer in 80 hours](https://www.wearedevelopers.com/videos/1511-how-we-built-an-ai-powered-code-reviewer-in-80-hours) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) ## Related Articles - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)