Data Engineer (Senior)

Overview

As a Senior Data Engineer in Central Data Platform, you build and govern a scalable data ecosystem that delivers a 360-degree view of customers across 60+ brands. You will design high-value data products and data architectures that serve BI, operations, and AI initiatives. This is a cross-brand role, enabling trustworthy data as the single source of truth. You’ll collaborate with Analytics, ML, and AI teams to drive measurable business value and transformative insights.

Responsabilità Design and deliver scalable data products providing a unified source of truth across 60+ brands

Build and optimize ETL/ELT pipelines for large-scale structured and unstructured data

Lead secure, cost-efficient data architecture design and enforce best practices in modeling, orchestration, and observability

Implement data governance with lineage, metadata management, and quality controls for a reliable semantic layer

Collaborate with Analytics, ML, and AI teams to translate business needs into technical solutions

Tune data pipelines for peak performance focusing on indexing, query optimization, and schema evolution

Mentor junior engineers and foster a culture of technical excellence

Requisiti fondamentali 7+ years in data engineering or data management

Advanced degree (Masters or PhD) in Computer Science, STEM, or related quantitative field

Proven track record deploying high-performance data solutions with measurable business value

Deep hands-on experience with Databricks (PySpark, Delta Lake, Unity Catalog) or equivalent table formats

Expert-level SQL with optimization and data modeling skills (Dimensional, Star Schema, Snowflake)

Proficiency in at least one cloud provider (AWS, Azure) and modern orchestration tools (Airflow, dbt)

Strong DevOps/Engineering skills (Docker, Kubernetes) and CI/CD with GitLab or GitHub

Experience with data versioning, schema evolution, and distributed metadata management

Strategic thinking

Effective communication with non-technical stakeholders

Problem-solving under fast-paced, multi-workstream environments

Databricks (PySpark, Delta Lake, Unity Catalog)

Advanced SQL and data modeling (Dimensional, Star Schema, Snowflake)

Cloud platforms (AWS, Azure)