Job Description

EXL is hiring a Senior Data Engineer to join a strategic AI / ML platform engagement with a leading specialty retailer. This is a hands-on build role embedded with the client's platform engineering team.

The role requires shipping production-grade data pipelines that feed real-time customer event data into machine learning workflows. The right person is comfortable owning the full lifecycle of pipeline design, build, and deployment: from streaming ingestion through event store design to model-ready feature delivery.

This is a high-visibility role with growth potential into a larger book of work as the engagement expands.

What You'll Do

• Design and operate event-driven data pipelines using Kafka consumers and Flink jobs to process high-volume customer events (clicks, purchases, returns) in near-real time.
• Build and optimize large-scale data transformations on Google Cloud Platform — BigQuery SQL, query performance tuning, and partitioning strategy at scale.
• Develop Python data engineering workloads using Polars or Pandas at scale, with rigorous attention to Parquet partitioning, join performance on large datasets, and memory efficiency.
• Build, deploy, and maintain ML pipeline components on Kubeflow Pipelines (KFP) and Vertex AI; package and deploy services with Docker.
• Design event store architecture: partitioning by customer, time-ordered event assembly across heterogeneous sources, and schema management for mixed event types.
• Partner with ML engineers, platform engineers, and data scientists to deliver clean, performant, model-ready data products.
• Document architecture decisions and contribute to engineering standards across the platform team.

Required Skills & Experience

• 6–12 years of experience in data engineering, platform engineering, or a closely related discipline.
• Streaming: Production experience with Kafka consumers and Flink stream processing — building, deploying, and operating streaming jobs at meaningful scale.
• GCP Data Stack: Strong SQL on BigQuery (or an equivalent cloud warehouse), with demonstrated query optimization, cost management, and partitioning chops.
• Python Data Engineering: Hands-on with Polars or Pandas at scale; deep working knowledge of Parquet partitioning and performance on large joins.
• ML Pipelines: Hands-on experience building and deploying components on Kubeflow Pipelines (KFP) and/or Vertex AI Pipelines; working proficiency with Docker.
• Event Store Design: Demonstrated experience designing event stores — partitioning by customer, time-ordered event assembly across sources, schema strategy for mixed event types (clicks, purchases, returns).
• Communication: Strong written and verbal communication; comfortable being the senior IC voice in design conversations with client stakeholders.

Nice to Have

• Domain experience in Retail or E-commerce — customer journey data, transaction analytics, returns and exchanges modeling.
• Exposure to schema registry tooling (e.g., Confluent), Iceberg, or Delta Lake.
• Experience working in client-facing or consulting engagements.
• Google Cloud certifications (Professional Data Engineer or equivalent).

Work Arrangement & Eligibility

• This role requires 3–4 days per week onsite in Seattle, WA. Fully remote and out-of-state candidates will not be considered.
• EXL is open to sponsoring H1B transfers for qualified candidates.