Senior Data Engineer

Node.Digital

Senior Data Engineer focusing on data architecture and ELT/ETL processes in Azure. Delivering robust data solutions with best practices and current technologies.

Posted 7/30/2026full-timeRemote • District of Columbia, Washington • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in designing and maintaining data architectures and pipelines in Azure environments, with a strong focus on ELT and ETL processes, SQL database management, and data normalization. Proficient in implementing source control and quality controls while leveraging modern data engineering practices and tools.

Highest-signal resume keywords

Data Architecture DesignELT and ETL Pipeline ImplementationAzure Synapse and Azure Machine LearningSQL and T-SQL Advanced OperationsDP-203 Certification

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Hard Skills

Data Engineering MethodsSQL Database ManagementPython ProgrammingPandasPySparkPolarsData NormalizationEntity ResolutionCode First DevelopmentData Pipeline Optimization

Soft Skills

Documentation SkillsAnalytical ThinkingProblem Solving

Tools & Technologies

Azure Data Lake StorageAzure SynapseAzure Machine LearningTerraformBicepREST APIsCI/CD Workflows

Certifications & Qualifications

DP-203Microsoft Certified Azure Data Engineer Associate

Industry Keywords

Data EngineeringCloud Based Data AnalyticsData ArchitectureData PipelinesAI Tooling

Tech Stack

Tools & technologies

AzureCloudETLPandasPySparkPythonSQLTerraform

About the role

Key responsibilities & impact

Provide authoritative expertise on data engineering methods and best practices, including code first development approaches and modern pipeline design patterns.
Design, implement, and maintain the data architecture that supports products and end users, with all assets managed under source control.
Design, implement, and maintain ELT and ETL pipelines for efficient processing of source data in Azure Synapse and Azure Machine Learning, using both SDK V1 and SDK V2.
Migrate source data identified by SBA OIG into Azure Data Lake Storage.
Normalize entity attributes such as addresses, phone numbers, and other common fields.
Review, maintain, and improve existing architecture and pipelines, including periodic audits addressing bottlenecks, deprecated dependencies, and architecture drift.
Establish quality controls across all pipelines and introduce error handling, logging mechanisms, and validation checks.
Incorporate source control across all pipelines and analytics codebases so code can evolve iteratively without destabilizing the architecture.
Optimize ingestion, processing, and storage across a wide variety of datasets and data types, including modern columnar formats such as Parquet.
Develop self service capabilities that let SBA OIG analysts query and export data for investigations and audits.
Author robust standard operating procedures governing the authoring, development, validation, publishing, execution, and monitoring of all data pipelines and assets in the Azure environment.
Produce detailed documentation of the data architecture, including data dictionaries, entity relationship diagrams, and pipeline process maps.
Maintain and expand the environment with additional datasets and services on request, following a defined intake and testing process before production deployment.
Stay current with emerging AI tooling relevant to data engineering and contribute to exploratory work evaluating automation and language model assisted capabilities.

Requirements

What you’ll need

Bachelor's degree in data engineering, computer science, data science, machine learning, mathematics, or a related field. Alternatively, five years of applied work experience in any of the same fields.
5 years - Maintaining SQL databases and conducting advanced operations in SQL and T-SQL.
5 years - Designing, implementing, and maintaining ELT and ETL processes in cloud based data analytics environments.
3 years - Working in Azure Synapse and Azure Machine Learning with the modern data stack. Certifications preferred, DP-203 or equivalent.
3 years - Manipulating data in Python. Pandas is required. PySpark and Polars preferred. Experience developing reusable, modular code preferred.
DP-203, Microsoft Certified Azure Data Engineer Associate, or an equivalent current certification preferred.
Implementing pipelines and infrastructure using code first approaches: Python SDK, CLI, REST APIs, or infrastructure as code tooling such as Terraform or Bicep.
Implementing source control and continuous integration and delivery workflows for data assets.
Demonstrated familiarity with AI coding assistants and large language model integration patterns.
PySpark or Polars at production scale.
Entity resolution and attribute normalization across records with inconsistent addresses, names, and identifiers.
Building self service analytic access for non-engineering users.

Benefits

Comp & perks

Medical
Dental
Vision
Basic Life
Health Saving Account
401K matching
Three weeks of PTO/Sick
11 Paid Holidays
Pre-Approved Online Training