Senior Associate Data Engineering Azure/Databricks Arlington 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 Associate, Data Engineering - Houston, hybrid We are seeking a hands-on Senior Data Engineer with strong expertise in Databricks, PySpark, Spark SQL, Delta Lake, Unity Catalog, and cloud-based data platforms. You will build scalable batch and streaming pipelines, implement Lakehouse solutions, and establish data governance controls that improve data quality, security, compliance, and trust across the analytics ecosystem Design, build, and optimize batch and streaming pipelines using Databricks, PySpark, Spark SQL, and Databricks Workflows. Develop scalable ingestion and ETL/ELT frameworks that integrate data from multiple enterprise sources. Implement Lakehouse architectures using Delta Lake and Medallion design patterns. Apply Unity Catalog for data cataloging, lineage, discovery, role-based access, and permission management. Establish automated data-quality controls, including validation, reconciliation, monitoring, and testing. Optimize Databricks workloads for performance, scalability, reliability, and cloud cost efficiency. Implement governance standards for metadata, classification, retention, privacy, security, and regulatory compliance. Support Git-based source control, CI/CD pipelines, and Infrastructure-as-Code deployments. Collaborate with data architects, analysts, data scientists, business stakeholders, data owners, and stewards. Document technical solutions, pipeline dependencies, and governance standards. Qualifications Your Skills & Experience: 5+ years of hands-on Data Engineering experience. 2+ years of hands-on Databricks experience. Strong proficiency in PySpark, Spark SQL, Databricks Workflows, Delta Lake, and Unity Catalog. Demonstrated experience building scalable data pipelines and integration solutions. Strong SQL, troubleshooting, and performance-optimization skills. Experience with Azure, AWS, or GCP data services. Practical knowledge of data governance, stewardship, metadata management, lineage, data quality, access controls, and compliance. Experience with Git, automated testing, CI/CD, and Infrastructure as Code. Strong communication skills with the ability to partner across technical and business teams. Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field. Preferred Qualifications Experience with Azure Data Factory, Airflow, or comparable orchestration tools. Experience with Kafka, streaming, or event-driven architectures. Knowledge of data mesh or domain-oriented architecture. Experience with Terraform, Azure DevOps, Power BI, or regulated enterprise environments. Databricks Data Engineer Associate or Professional certification. Set Yourself Apart With: Developer certifications for AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or related cloud/data platforms. Demonstrated experience applying AI engineering concepts in practical business environments rather than only academic or research settings. Hands-on experience supporting AI/ML and LLM lifecycle needs such as model deployment support, monitoring, validation, shadow deployments, release management, and evaluation or data quality measurement for both predictive models and generative systems. Experience in retail, financial services, energy, CPG, logistics, manufacturing, or other data-rich industries where applied AI and large-scale data engineering are used to solve operational or client-facing problems. Understanding of Agile, product, and delivery methodologies in consulting or client-facing environments. Additional information Salary Range: $104,000-120,000. The range shown represents a grouping of ranges currently in use at Publicis Sapient. Actual range for this position may differ, depending on location and specific skillset required for the work. Benefits of Working Here 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. 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
Your Skills & Experience: 5+ years of hands-on Data Engineering experience. 2+ years of hands-on Databricks experience. Strong proficiency in PySpark, Spark SQL, Databricks Workflows, Delta Lake, and Unity Catalog. Demonstrated experience building scalable data pipelines and integration solutions. Strong SQL, troubleshooting, and performance-optimization skills. Experience with Azure, AWS, or GCP data services. Practical knowledge of data governance, stewardship, metadata management, lineage, data quality, access controls, and compliance. Experience with Git, automated testing, CI/CD, and Infrastructure as Code. Strong communication skills with the ability to partner across technical and business teams. Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field. Preferred Qualifications Experience with Azure Data Factory, Airflow, or comparable orchestration tools. Experience with Kafka, streaming, or event-driven architectures. Knowledge of data mesh or domain-oriented architecture. Experience with Terraform, Azure DevOps, Power BI, or regulated enterprise environments. Databricks Data Engineer Associate or Professional certification. Set Yourself Apart With: Developer certifications for AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or related cloud/data platforms. Demonstrated experience applying AI engineering concepts in practical business environments rather than only academic or research settings. Hands-on experience supporting AI/ML and LLM lifecycle needs such as model deployment support, monitoring, validation, shadow deployments, release management, and evaluation or data quality measurement for both predictive models and generative systems. Experience in retail, financial services, energy, CPG, logistics, manufacturing, or other data-rich industries where applied AI and large-scale data engineering are used to solve operational or client-facing problems. Understanding of Agile, product, and delivery methodologies in consulting or client-facing environments.