Dynatron Software

Sr. Data Engineer

Vaga remota de Senior Data Engineer com fit claro de localização do candidato.

Publicada3 de jul. de 2026

Países elegíveis1 país aceito

Sinal de senioridadeSenior

Modelo de trabalhoRemoto

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Índia

AWS Python Snowflake SQL

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Atualidade da fonte3 de jul. de 2026

Fit de localização1 país aceito

Match de stackAWS, Python

Caminho de aplicaçãoSite da empresa

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Match de stackAdicione skills ao perfil para compararAWS, Python

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Aplicando paraSr. Data EngineerDynatron Software

Fit de país1 país aceito

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Resumo da vaga

Sr. Data Engineer

Requisitos e responsabilidades

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Work Hours & Collaboration Expectations

9:00 AM – 2:00 PM EST
8:00 AM – 1:00 PM CST
7:00 AM – 12:00 PM MST
6:00 AM – 11:00 AM PST

What You’ll DoPipeline Development & AWS Data Lake Engineering

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.

What You’ll DoPipeline Development & AWS Data Lake Engineering

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.

What You’ll DoPipeline Development & AWS Data Lake Engineering

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.

What You’ll DoPipeline Development & AWS Data Lake Engineering

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.

What You’ll DoPipeline Development & AWS Data Lake Engineering

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.

What You’ll DoPipeline Development & AWS Data Lake Engineering

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,

This role rewards data engineers who:

Build scalable, reliable, and secure data systems that support real business outcomes.
Operate with urgency, ownership, and strong engineering discipline.
Think beyond individual pipelines to improve platform quality, observability, and long-term maintainability.
Partner effectively across technical and business teams.

This role rewards data engineers who:

Help Dynatron turn trusted data into smarter products, better decisions, and stronger customer outcomes.and follow through reliably.

Why Dynatron

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.

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