Senior Data Engineer

Dynatron Software, Inc.

Senior Data Engineer managing data pipelines, optimizing data lakes, and collaborating with AI/ML teams at Dynatron. Focused on building robust data ecosystems and ensuring high data quality and performance.

Posted 7/7/2026full-timeRemote • 🇮🇳 IndiaSenior💰 ₹4,200,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining complex data pipelines using AWS technologies, with a strong focus on data validation, optimization, and collaboration with cross-functional teams. Proficient in Python, SQL, and data modeling techniques to support scalable data solutions and machine learning readiness.

Highest-signal resume keywords

AWS GluePythonSQLKinesisDatabricks

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringETLData ValidationDimensional ModelingStreaming ApplicationsData ProfilingFeature StoresModular Data StructuresAutomated Testing FrameworksSQL Optimization

Soft Skills

MentoringDocumentationOwnership Mindset

Tools & Technologies

AWS S3AWS Step FunctionsDatabricks WorkflowsSnowflakeAWS KinesisKafka

Certifications & Qualifications

SnowPro CoreDatabricks Certified Data Engineer ProfessionalAWS Certified Data Engineer

Industry Keywords

Medallion ArchitectureData LakeHigh-Throughput ProcessingCost-Effective ProcessingLarge-Scale Distributed Systems

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).

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.