Senior Data Engineer - Arlington, VA Hybrid

Company description Publicis Sapient is a digital transformation partner helping established organizations get to their future, digitally enabled state, both in the way they work and the way they serve their customers. We help unlock value through a start-up mindset and modern methods, fusing strategy, consulting, and customer experience with agile engineering and problem-solving creativity. United by our core values and our purpose of helping people thrive in the brave pursuit of next, our 20,000+ people in 53 offices around the world combine experience across technology, data sciences, consulting, and customer obsession to accelerate our clients’ businesses through designing the products and services their customers truly value. Overview Senior Data Engineer - Arlington, Hybrid As a Senior Data Engineer you will create and enhance scalable data pipelines using established ingestion frameworks, orchestrate workflows with Azure Data Factory, and develop transformations that load trusted data into an Enterprise Data Lake, Delta Lake, and Azure Synapse Analytics dedicated SQL pools. This role also owns testing, technical documentation, data quality configuration, performance optimization, maintenance, and production support for enterprise data solutions. Your Impact • Create new batch and streaming data pipelines by leveraging and extending existing data ingestion frameworks and tools. • Orchestrate, schedule, and monitor data pipelines using Azure Data Factory. • Develop and enhance data transformations based on business and technical requirements using PySpark, Python, and SQL. • Parse, transform, and load data into the Enterprise Data Lake, Delta Lake, and Azure Synapse Analytics dedicated SQL pools. • Build and maintain Databricks data engineering solutions, including Delta Live Tables (DLT), compute configuration, and pipeline maintenance. • Configure and maintain data quality rules, validation controls, reconciliation processes, monitoring, and automated alerts. • Perform unit testing and coordinate integration testing and user acceptance testing (UAT) with business and technology partners. • Create and maintain high-level designs (HLDs), detailed designs (DDs), data mappings, operational runbooks, and support documentation. • Tune and optimize Databricks, Spark, SQL, and Synapse workloads for performance, reliability, scalability, and cost efficiency. • Provide production support, including incident troubleshooting, root-cause analysis, defect resolution, and operational maintenance. • Use Azure DevOps and Git to support source control, CI/CD, release management, and controlled production deployments. • Collaborate with data architects, analysts, business stakeholders, and support teams to deliver reliable enterprise data products. Qualifications Your Skills & Experience • Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field. • 4+ years of data engineering experience, including hands-on delivery and support of enterprise data pipelines. • 2+ years of hands-on Azure Databricks data engineering experience. • Strong experience with Databricks, Delta Live Tables (DLT), Delta Lake, PySpark, Python, SQL, and Spark SQL. • Strong experience orchestrating data pipelines with Azure Data Factory. • Experience loading and serving data through Enterprise Data Lake, Delta Lake, and Azure Synapse Analytics dedicated SQL pools. • Experience configuring Databricks compute, data quality rules, monitoring, maintenance processes, and workload optimization. • Experience performing unit testing and coordinating integration testing and UAT. • Experience creating HLDs, detailed designs, data pipeline documentation, and operational runbooks. • Experience with Azure DevOps, Git, CI/CD pipelines, and production deployment practices. • Demonstrated experience supporting production data platforms and resolving data pipeline incidents. Additional information Salary Range: $109,000-130,000 The range shown represents a grouping of relevant ranges currently in use at Publicis Sapient. Actual range for this position may differ, depending on location and specific skillset required for the work. As part of our dedication to an inclusive and diverse workforce, Publicis Sapient is committed to Equal Employment Opportunity without regard for race, color, national origin, ethnicity, gender, protected veteran status, disability, sexual orientation, gender identity, or religion. We are also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, you may contact us at An inclusive workplace that promotes diversity and collaboration. Access to ongoing learning and development opportunities. Competitive compensation and benefits package. Flexibility to support work-life balance. Comprehensive health benefits for you and your family. Generous paid leave and holidays. Wellness program and employee assistance. #LI-Hybrid
Your Skills & Experience • Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field. • 4+ years of data engineering experience, including hands-on delivery and support of enterprise data pipelines. • 2+ years of hands-on Azure Databricks data engineering experience. • Strong experience with Databricks, Delta Live Tables (DLT), Delta Lake, PySpark, Python, SQL, and Spark SQL. • Strong experience orchestrating data pipelines with Azure Data Factory. • Experience loading and serving data through Enterprise Data Lake, Delta Lake, and Azure Synapse Analytics dedicated SQL pools. • Experience configuring Databricks compute, data quality rules, monitoring, maintenance processes, and workload optimization. • Experience performing unit testing and coordinating integration testing and UAT. • Experience creating HLDs, detailed designs, data pipeline documentation, and operational runbooks. • Experience with Azure DevOps, Git, CI/CD pipelines, and production deployment practices. • Demonstrated experience supporting production data platforms and resolving data pipeline incidents.