Senior Data Engineer

🕒 il y a 7 jours

🏛️ District of Columbia, Washington – Distant

🏛️ District of Columbia – Remote ☕ Washington – Remote

⏰ Temps Plein

🟠 Senior

🚰 Ingénieur Data

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Node.Digital

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11 - 50 employés

💼 Conseil

📣 Marketing

📦 Logistique

Consulting • Marketing • Logistics

Nous aimons créer des solutions numériques innovantes en utilisant l'automatisation et l'intelligence artificielle/apprentissage automatique pour résoudre des problèmes complexes pour la mission de nos clients.

Description

• 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.

🎯 Exigences

• 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.

🏖️ Avantages

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