Senior Data Engineer - Databricks

UNAVAILABLE; McLean (VA); Presencial; USD 120.000 - 160.000; Jornada completa

Descripción de la vacante

Unknown Company seeks a seasoned Senior Data Engineer to join the team and help build enterprise-grade data platforms, data lakes, and lakehouses using Databricks. You will collaborate with clients, design scalable pipelines, and support cloud deployments across AWS, Azure, or GCP.

The role requires strong communication skills, hands-on Databricks and PySpark expertise, and experience in an Agile environment. Security clearance may be required.

Formación

• Ability to obtain and maintain a U.S. government security clearance
• 5–7 years of industry experience developing production software and solving complex technical problems
• 5–7 years of direct experience in Data Engineering with Databricks, Spark, and Delta Lake
• Experience with cloud data platforms (AWS, Azure, GCP) and data pipelines

Responsabilidades

• Lead and support the design, development, and migration of data lake and lakehouse environments using Databricks, focusing on performance, reliability, and scalability.
• Assess and understand existing ETL jobs, workflows, data marts, BI tools, reports, and data dependencies.
• Design, develop, and optimize scalable data pipelines and data processing solutions using Databricks.
• Address technical inquiries concerning customization, integration, enterprise architecture, and data product features.
• Develop and support databases, data warehouses, data marts, data lakes, and lakehouses in cloud environments (AWS/Azure/GCP).
• Apply hands-on Databricks, SQL, PySpark, and cloud technologies to support data engineering solutions.
• Support an Agile software development lifecycle.
• Contribute to the continued growth of our AI & Data Exploitation Practice.

Conocimientos

Excellent communication

Customer service

Agile development experience

Educación

Bachelor's degree in computer science, information systems, engineering, or related technical discipline

Herramientas

Databricks

Apache Spark

Delta Lake

SQL (T-SQL / PostgreSQL / MySQL)

Airflow

AWS

Azure

Python / PySpark

Java

Scala

Iceberg

Descripción del empleo

Overview

We are looking for a seasoned Senior Data Engineer to work with our team and clients to develop enterprise-grade data platforms, data lakes and lakehouses, services, and pipelines using Databricks. We are looking for more than just a Senior Data Engineer; we are seeking a technologist with excellent communication and customer service skills and a passion for data and problem solving.

Responsibilities

• Lead and support the design, development, and migration of data lake and lakehouse environments using Databricks, with a focus on performance, reliability, and scalability.
• Assess and understand existing ETL jobs, workflows, data marts, BI tools, reports, and associated data dependencies.
• Design, develop, and optimize scalable data pipelines and data processing solutions using Databricks.
• Address technical inquiries concerning customization, integration, enterprise architecture, and the features and functionality of data products.
• Develop and support database, data warehouse, data mart, data lake, and lakehouse solutions in cloud environments such as AWS, Azure, or GCP.
• Apply hands‑on expertise with Databricks, SQL, PySpark/Python, and cloud technologies to support data engineering solutions.
• Support an Agile software development lifecycle.
• Contribute to the continued growth of our AI & Data Exploitation Practice.

Qualifications

• Ability to obtain and maintain a U.S. government security clearance.
• Bachelor's degree in computer science, information systems, engineering, or a related technical discipline.
• 5–7 years of industry experience developing production software and solving complex technical problems.
• 5–7 years of direct experience in Data Engineering, including experience with tools and technologies such as:
  • Big data technologies: Databricks, Apache Spark, Delta Lake, or similar.
  • Relational SQL: preferably T‑SQL; alternatively PostgreSQL or MySQL.
  • Data pipeline and workflow management tools: Databricks Workflows, Airflow, AWS Step Functions, or similar.
  • Cloud services: Databricks on AWS, S3, EC2, RDS, or Azure equivalents.
  • Object‑oriented or scripting languages: PySpark/Python, Java, C++, Scala, or similar.
  • Data lakehouse architecture and technologies such as Delta Lake or Apache Iceberg.
• Advanced SQL knowledge and experience working with relational databases, including query development and optimization, as well as familiarity with a variety of database technologies.
• Experience manipulating, processing, and extracting value from large, disparate datasets.
• Ability to assess existing data pipelines, understand their purpose and functionality, and efficiently reimplement or modernize them using Databricks.
• Experience working with structured and unstructured data.
• Experience architecting data systems, including transactional systems and data warehouses.
• Experience with the software development lifecycle (SDLC), CI/CD practices, and development, test, and production environments.
• Experience with data cataloging tools such as Informatica EDC, Unity Catalog, Collibra, Alation, Microsoft Purview, or Amazon DataZone is a plus.
• Demonstrated commitment to data governance and data management best practices.
• Experience working in an Agile environment.
• Experience supporting project teams of developers and data scientists building web‑based interfaces, dashboards, reports, analytics solutions, and machine learning models.