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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Manager Data Engineering - **Company:** Sapient Corporation - **Location:** Arlington, VA, United States - **Experience:** Expert - **Salary:** $160,000.0 - $262,000.0 - **Contract:** Permanent contract - **Skills:** Agile Methodology, Artificial Intelligence, Amazon Web Services, Automated Storage and Retrieval Systems, Automation of Tests, Microsoft Azure, Big Data, BigTable, BigQuery, Databases, Continuous Integration, Data as a Services, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Transformation, Data Systems, Amazon DynamoDB, Data Flow Control, Graph Database, Python (Programming Language), Machine Learning, Microsoft SQL Server, MySQL, NoSQL, Operational Databases, Oracle (Applications), Regression Testing, Release Management, Azure Machine Learning, Search Technologies, Data Streaming, Systems Integration, Software Repository, Data Processing, Google Cloud, Azure Data Factory, Retrieval-Augmented Generation, Large Language Models, Snowflake, Apache Spark, Data Lakes, AI Platforms, Cosmos DB, Spark Streaming, Data Management, Machine Learning Operations, Vertica, Functional Programming, Data Pipelines, Amazon Redshift, Databricks - **Published:** September 10, 2026 - **Apply:** https://jobs.localjobnetwork.com/apply/add/88285241/1 ## About the Role * Demonstrable experience implementing end-to-end data pipelines and production-grade data platforms. * Hands-on experience with at least one leading public cloud data platform: Amazon Web Services, Microsoft Azure, or Google Cloud Platform; * Experience with Databricks as a data engineering platform is strongly preferred, including working with notebooks, jobs, Delta Lake, or similar lakehouse patterns. * Strong Python proficiency and practical experience using Python-based tooling for data engineering, automation, platform development, or AI engineering workflows. * Implementation experience with column-oriented database technologies such as BigQuery, Redshift, Vertica, or similar platforms; NoSQL database technologies such as DynamoDB, Bigtable, Cosmos DB, or similar; and traditional database systems such as SQL Server, Oracle, or MySQL. * Experience implementing data pipelines for both streaming and batch integrations using tools and frameworks such as Glue ETL, Lambda, Google Cloud Dataflow, Azure Data Factory, Spark, Spark Streaming, or similar technologies. * Experience with data modeling, warehouse design, fact/dimension implementations, and modern lakehouse or data mesh patterns. * Experience with code repositories, continuous integration, automated testing, release management, and production support practices. * Familiarity with MLOps concepts and the data engineering responsibilities required to support AI/ML deployment, validation, monitoring, rollback, and operational reliability. * Ability to handle module or track-level responsibilities while contributing to tasks hands-on. * Good communication skills and willingness to work as part of a collaborative, cross-functional team. AI Engineering & Modern Data Platform Experience: * Exposure to AI engineering patterns, including context engineering, retrieval-augmented generation support patterns, agent architectures, and production data services that support AI-enabled experiences. * Experience building and maintaining the pipelines behind retrieval systems, including document parsing, chunking, metadata extraction, embedding generation, and incremental reindexing, alongside the vector databases, graph databases, semantic search, and knowledge retrieval structures they feed. * Exposure to agentic platforms or cloud AI services such as Vertex AI, Azure AI services, AWS AI services, or comparable platforms; specific platform experience is less important than understanding how AI engineering differs from traditional data engineering. * Practical experience deploying agents, integrating agent frameworks, or supporting agentic workflows in production or near-production environments is a plus. * Experience building evaluation data infrastructure for AI systems, including ground-truth and golden datasets, offline evaluation pipelines, and the data scaffolding behind LLM-as-judge and regression testing. * Experience modeling and persisting agent state, including session context, conversation history, and memory stores, treating them as a durable storage and data modeling problem rather than an application detail. * Support AI-enabled engineering use cases by designing high-quality data foundations, retrieval patterns, context engineering approaches, and scalable data services that power agentic and machine learning solutions, applying the same lineage, provenance, and data contract rigor to context and retrieval sources that you would to a production warehouse. * Experience with agentic harnesses or orchestration tools such as Pi, Hermes Agent, or similar platforms is a plus, but not required. * Experience with Snowflake and zero-copy architecture patterns is a plus, particularly for retail, financial services, energy, or CPG-oriented use cases. 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. ## Description As a Senior Manager Data Engineering (Technology Architect), you will be responsible for designing and implementing scalable, high-performance data platforms that enable data-driven decision-making. You will work closely with cross-functional teams to architect, build, and optimize data solutions that support business objectives and drive innovation. Your Impact: * Combine your technical expertise and problem-solving passion to work closely with clients, turning complex ideas into end-to-end data solutions that transform our clients' business. * Translate client requirements into system design and develop solutions that deliver measurable business value. * Lead, design, develop and deliver large-scale data systems, data processing, data transformation, and data platform modernization initiatives. * Build and optimize batch and streaming data pipelines across modern cloud data platforms and distributed processing frameworks. * Support AI-enabled engineering use cases by designing high-quality data foundations, retrieval patterns, context engineering approaches, and scalable data services that power agentic and machine learning solutions. * Automate data platform operations and manage post-production systems, observability, quality, reliability, and operational processes, including telemetry pipelines that capture prompt, response, trace, latency, token, and cost data for AI-enabled services in a queryable form. * Conduct technical feasibility assessments and provide project estimates for the design and development of solutions. * Mentor, support, and grow junior team members while contributing hands-on to delivery. ## Related Videos - [Leveraging Real time data in FSIs](https://www.wearedevelopers.com/videos/806-leveraging-real-time-data-in-fsis) - [MySQL Protocol Features You Should Be Aware Of](https://www.wearedevelopers.com/videos/100267-mysql-protocol-features-you-should-be-aware-of) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [NoSQL Data Modeling for Front-end Developers](https://www.wearedevelopers.com/videos/297-nosql-data-modeling-for-front-end-developers) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) - [Coding for Good: Achieving social change with an app](https://www.wearedevelopers.com/videos/1645-coding-for-good-achieving-social-change-with-an-app) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Got AI ideas but no money? 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