Data Engineer, Forward Deployed Engineer

Kyndryl•Dallas, TX

1d•$143,640 - $327,600•Hybrid

About The Position

At Kyndryl, we design, build, manage and modernize the mission-critical technology systems that the world depends on every day. The Role At Kyndryl, we don't just build technology—we deploy it where it matters most, driving scalable technology modernization and mission-critical transformations for the world's leading enterprises. Within our newly established Lab, we design, build, and deploy real-world enterprise technology where it creates the greatest physical impact. We operate under a modern triad delivery model, combining deep engineering, design, and business strategy to deliver outcomes that keep global industries moving forward. As a Forward Deployed Data Engineer, you are the builder whom customers ask for by name. You will serve as a critical bridge between complex business challenges and scalable data solutions, operating at the intersection of AI innovation and real-world impact. Operating as a technical anchor, you will design, deploy, and refine the high-performance data infrastructure, pipelines, and context engines that fuel next-generation artificial intelligence, machine learning, and autonomous workflows in live customer environments. This is a dynamic, highly collaborative, and hands-on role. Operating in a hybrid model out of our Lab with a minimum of three days in-office, you will work directly alongside architects, software engineers, and client stakeholders in rapid-prototype cycles. You will take true ownership of outcomes, moving quickly from scoping and building initial proof-of-concepts to deploying hardened, production-ready systems that generate immediate business value for our customers.

Requirements

Strong, production-grade coding proficiency in Python and advanced SQL optimization.
Practical experience designing, building, and orchestrating ETL/ELT pipelines using tools such as Airflow, dbt, and Kafka.
Extensive technical expertise in database modeling, distributed computing, and deploying resources within cloud-native environments (AWS, Azure, or GCP).
Hands-on experience implementing vector databases (e.g., Pinecone, Milvus, Chroma, Weaviate) and vector indexing strategies for high-context search.
Working knowledge of data quality, pipeline lineage, and modern data observability tools (e.g., Great Expectations, DataHub).
Cloud-native technical certifications on AWS, Azure, or GCP, or a demonstrated willingness to achieve certifications during your journey with us.
Bachelor’s degree in Computer Science, Data Science, Engineering, or a closely related technical field, or equivalent practical professional experience.

Nice To Haves

Prior experience designing and implementing data pipelines in highly regulated industries, such as Financial Services, Healthcare, or the Public Sector.
Hands-on experience scaling deployments using containerization tools such as Docker and Kubernetes.
Familiarity with emerging semantic modeling techniques, metadata management, and modern integration patterns for AI agent architectures.
Experience building and monitoring CI/CD pipelines, utilizing version control (Git & GitHub), and delivering software under Agile methodologies.
Advanced technical certifications in specialized database engineering, data architecture, or machine learning.
Master's degree in Computer Science, Data Engineering, or a related discipline.

Responsibilities

Design, optimize, and maintain scalable ETL/ELT pipelines to support high-volume batch processing and low-latency real-time streaming.
Architect distributed systems and database models across hybrid and cloud-native environments, ensuring they are optimized for performance, scale, and cost.
Diagnose, debug, and resolve complex performance bottlenecks across diverse database schemas and distributed data layers.
Deploy, index, and manage vector databases and semantic layers specifically tailored for high-context AI search, retrieval-augmented generation (RAG), and agent memory structures.
Construct robust, secure, and high-throughput API endpoints that enable autonomous systems to interact dynamically with complex, distributed datasets.
Design and configure feature stores to support active machine learning pipelines and real-time inference loops.
Implement modern data quality and pipeline observability frameworks to ensure clean, high-fidelity data delivery to downstream systems.
Log pipeline activities, track metadata lineage, and set up automated testing loops to systematically prevent schema drift.
Ensure all built technical solutions align seamlessly with strict industry compliance, security, and data protection regulations.
Capture deployment insights, document real-world best practices, and feed these learnings back into Kyndryl’s core technology platforms and frameworks to accelerate future customer implementations.
Collaborate seamlessly across global teams and practices, cutting through organizational silos to prioritize the right customer outcome.
Balance rapid software delivery and field prototyping with long-term platform stability, reducing technical debt as solutions mature.