Senior Data Engineer

Fractal Analytics•California, CA

3d•$130,000•Hybrid

About The Position

Fractal is seeking a Senior Data Engineer for one of its clients, a leading global digital retail and e-commerce organization. This role involves designing, building, and operating large-scale, production-grade data platforms to support analytics and data science within the Retail Online organization. It is a hands-on engineering position requiring expertise in data pipeline architecture, analytics engineering, and production operations. The engineer will collaborate with Data Science, Analytics, and Business teams to define initiatives, manage dependencies, and deliver reliable data assets. The ideal candidate is a strong technologist and a delivery-minded engineer capable of managing cross-functional programs, enforcing quality standards, translating complex methodologies, and ensuring pipeline reliability, scalability, and cost-efficiency.

Requirements

10+ years of software development and data engineering experience.
Very high proficiency in Python (production-grade: packaging, testing, CI/CD).
Detailed knowledge of and substantial experience with data structures and algorithms.
Solid technical database knowledge across Hadoop, Python, and Snowflake (data modeling, performance tuning, cost governance, large-scale query optimization).
Experience working with large-scale data warehouse and data lake solutions (Teradata, Snowflake, Redshift, Hadoop/HDFS-based ecosystems).
Hands-on Kubernetes experience (deploying data workloads, managing namespaces, debugging pods/logs/events, tuning resource requests and limits).
Proficiency with Docker (authoring Dockerfiles, multi-stage builds, container registries, integrating images into orchestration and CI/CD pipelines).
Experience owning data operations and support (on-call rotations, SLA management, incident response, runbook authorship, postmortem processes).
Hands-on experience with Tableau (published data sources, extract schedules, performance optimization).
Experience with Continuous Integration & Delivery and automation tools (Jenkins, Artifactory, Git).
Hands-on experience in a Unix/Linux environment.
Experience with Agile and Test-Driven Development methodology.
Ability to present complex ideas in a clear, concise way to both technical and non-technical audiences.
BS in Computer Science, Engineering, Mathematics, Statistics, Econometrics, or other quantitative field.

Nice To Haves

MS in Computer Science, Engineering, Statistical Methods, or Machine Learning.

Responsibilities

Plan project scope, timelines, and dependencies across Data Science, Analytics, and Business teams, tracking critical paths and coordinating deliverables.
Maintain project tracking artifacts and status dashboards, preparing and delivering executive roadmap updates, milestone progress, and risk mitigation summaries.
Directly coordinate with offshore engineering teams, aligning on technical designs, enforcing development standards, and leading code and documentation reviews.
Define delivery milestones, manage scope changes, enforce operational SLAs, and lead cross-team incident escalations and post-incident reviews.
Develop, validate, and maintain Tableau dashboards and metric logic as tested, version-controlled production software, enforcing pre-release QA validation gates.
Partner with Data Scientists to validate data pipelines for forecasting, causal inference, incrementality, and MMM, ensuring reproducibility for model development.
Oversee QA for A/B testing infrastructure, experiment tracking, feature stores, and inference workflows, auditing tracking mechanisms for consistency.
Audit retail customer behavior data transformations against operational logic and translate complex statistical methodologies into validated, actionable insights.
Design and optimize scalable, automated ETL/ELT pipelines to deliver production-grade data assets.
Build and maintain monitoring systems for pipeline health, data quality, and cost spikes.
Containerize data applications with Docker and integrate into CI/CD workflows, deploying and managing workloads on Kubernetes.
Own job scheduling, run-book maintenance, and incident triage with a support-first mindset, resolving failures and maintaining playbooks.