Data Engineer

Description

We are looking for a Data Engineer to join a team building dependable, scalable data solutions in Arlington, Virginia. This role focuses on designing modern data pipelines, improving the reliability of data platforms, and supporting analytics and operational needs across the business. The ideal candidate brings strong engineering depth, experience with cloud-based data ecosystems, and the ability to work closely with cross-functional partners to deliver high-quality data products.

Responsibilities:
Design, build, and maintain scalable data pipelines and processing workflows that support reliable access to business-critical data.
Develop reusable data platforms and automation solutions that improve the efficiency, consistency, and performance of data operations.
Produce clear, maintainable, and well-documented code for data integration, transformation, and platform services.
Establish automated validation and testing practices to monitor accuracy, completeness, and overall data quality.
Partner with architects, product leaders, data scientists, and DevOps teams to deliver resilient data systems aligned with technical and business goals.
Assess new data sources, determine their value and fit, and implement effective ingestion approaches.
Deploy and integrate data management capabilities within enterprise or client environments while meeting operational requirements.
Strengthen data protection and continuity by identifying risks, supporting backup strategies, and contributing to recovery planning.
Build and support data storage solutions such as warehouses, lakes, and operational repositories for reporting and advanced analytics.

Requirements

At least 7 years of experience in data engineering or a closely related technical discipline.
Demonstrated success building large-scale, production-ready data pipelines and data processing solutions.
Strong programming skills in Python and solid experience with SQL and workflow orchestration tools such as Airflow and dbt.
Hands-on experience with Databricks in a cloud environment, including pipeline development, distributed processing, and performance tuning.
Practical knowledge of big data technologies such as Apache Spark, Kafka, and Hadoop, along with modern data formats including Parquet, Delta Lake, or Iceberg.
Experience working with cloud data services and platforms such as object storage, managed ETL tools, analytical databases, or comparable Azure and Google Cloud offerings.
Solid understanding of data modeling, secure data architecture, and optimization techniques for high-performing data stores.
Ability to collaborate effectively in Agile teams and communicate clearly with both technical stakeholders and business partners.

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