Senior Data Engineer

Coderio

. Design, build, and deploy scalable, decoupled, high-performance data pipelines for batch and streaming ingestion into Data Lakes and Data Warehouses .

Posted 10/22/2026full-timeRemote • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and optimizing scalable data pipelines using Python, PySpark, and advanced SQL, while applying MLOps and DevOps best practices. Proficient in managing cloud environments such as AWS or GCP and maintaining CI/CD pipelines with Docker.

Highest-signal resume keywords

Data Engineering ExperiencePython MasteryAdvanced SQL ProficiencyAWS or GCP ExperienceMLOps Familiarity

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DesignETL/ELT DevelopmentQuery OptimizationData Structures UnderstandingBatch/Streaming ProcessingInfrastructure as CodeREST API DevelopmentData Quality FrameworksPerformance TuningMLOps Workflows

Soft Skills

Excellent Communication Skills

Tools & Technologies

PySparkDockerGitCI/CD PipelinesAWS RedshiftAWS GlueAWS EMRAWS S3GCP BigQueryTerraform

Industry Keywords

Data LakesData WarehousesCloud StorageData GovernanceData IntegrityData ProductsModel Interaction DataGenerative AI Data PipelinesPrompt Engineering WorkflowsMLOps Lifecycle

Tech Stack

Tools & technologies

Amazon RedshiftAWSBigQueryCloudDockerETLGoogle Cloud PlatformPySparkPythonSQLTerraformUnix

About the role

Key responsibilities & impact

Design, build, and deploy scalable, decoupled, high-performance data pipelines for batch and streaming ingestion into Data Lakes and Data Warehouses
Develop and optimize ETL/ELT pipelines using Python, PySpark, and advanced SQL
Partner with Data Scientists and ML Engineers to build resilient pipelines, automate MLOps workflows, and gather model interaction data for retraining loops
Monitor, troubleshoot, and optimize pipeline execution, query performance, and cloud storage resource utilization
Implement data quality frameworks and testing controls for data integrity, reliability, and governance
Apply MLOps and DevOps best practices
Maintain CI/CD pipelines and use Docker for containerization
Document technical standards and architectures
Enable Data Products throughout their lifecycle, including sourcing data via batch and stream processing and optimizing data architecture

Requirements

What you’ll need

Must be based in the United States
Must be authorized to work in the United States
5+ years of experience in Data Engineering
Proven track record of designing and running large-scale data systems
Mastery of Python
Advanced SQL, including query optimization and performance tuning
Hands-on experience with AWS (Redshift, Glue, EMR, MWAA, S3) or GCP (BigQuery)
Experience with modern data processing engines, including PySpark
Proficiency with UNIX environments, Docker, Git, and CI/CD pipelines
Solid understanding of data structures, batch/streaming processing
Familiarity with MLOps or the ML deployment lifecycle
Excellent verbal and written English communication skills
Experience with Infrastructure as Code, such as Terraform, nice to have
Experience building and maintaining scalable REST APIs, nice to have
Exposure to prompt engineering workflows or generative AI data pipelines, nice to have

Benefits

Comp & perks

Remote-first company
Fair compensation
Inclusive, challenging environment
Real opportunities for growth
Remote / hybrid work arrangement