Senior Data Engineer

Dynatron Software, Inc.

Senior Data Engineer crafting and maintaining data pipelines to support analytics, AI, and reporting at Dynatron. Collaborating with product and machine learning teams within a remote-first environment.

Posted 7/1/2026full-timeRemote • 🇺🇸 United StatesSenior💰 $45,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and maintaining complex data pipelines using AWS Glue, Kinesis, and Databricks, while ensuring high-quality data through automated validation and QA processes. Proficient in Python, SQL, and advanced data modeling techniques to support scalable data solutions and collaboration with cross-functional teams.

Highest-signal resume keywords

AWS GluePythonSQLKinesisDatabricks

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data EngineeringETLELTData ValidationDimensional ModelingMedallion ArchitectureStreaming ApplicationsData ProfilingFeature StoresSQL Optimization

Soft Skills

Strong CommunicationMentoringCollaborative MindsetOwnership MindsetDocumentation Habits

Tools & Technologies

AWS S3AWS Step FunctionsAWS KinesisKafkaSnowflakeDatabricks Workflows

Certifications & Qualifications

SnowPro CoreDatabricks Certified Data Engineer ProfessionalAWS Certified Data Engineer

Industry Keywords

Data PipelinesData LakeHigh-Throughput ProcessingAutomated Testing FrameworksOperational Discipline

Tech Stack

Tools & technologies

AWSDistributed SystemsETLKafkaPySparkPythonSQL

About the role

Key responsibilities & impact

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.
Develop and deploy real-time ingestion pipelines using AWS Kinesis or Kafka.
Own end-to-end data validation and QA by building automated data quality checks directly into the ETL/ELT pipelines.
Engineer ML-ready datasets and manage Feature Stores to support the Data Science team.
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.

Requirements

What you’ll need

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.
Relevant IT professional certifications, such as SnowPro Core, Databricks Certified Data Engineer Professional, or AWS Certified Data Engineer (Nice-to-Have).
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.

Benefits

Comp & perks

Opportunity to build and scale the data foundation of a growing, AI-enabled SaaS company
High-impact role supporting real-time analytics, machine learning, enterprise reporting, and product innovation
Close partnership across Data, Product, Engineering, Analytics, and business leadership
Values-driven culture built on accountability, urgency, and delivering measurable results
Remote-first environment offering flexibility, autonomy, and trust