Lead Data Engineer - Databricks, Azure, & Data at Scale

84.51; Chicago (IL); Vor Ort; USD 125.000 - 207.000; Vollzeit

Benefits dieser Stelle

Health Insurance

401(k) with match

Paid time off

Zusammenfassung

Lead Data Engineer at 84.51° will design and implement solutions to ingest, store, transform, and distribute big data on Azure Cloud. You will work with Databricks (Spark, SQL, PySpark), Databricks Workflows, Python, JSON, and SQL to power customer personalization at scale.

Collaboration with Product and Data Science is critical; mentoring junior staff and leading Databricks-based projects are key expectations in this role.

Qualifikationen

• 5+ years proven ability of professional data development experience.
• Strong understanding of Agile Principles (Scrum).
• 5+ years proven ability of developing with Spark (Databricks, PySpark).
• 3+ year developing experience with Python.
• 1+ year developing experience with REST APIs.
• 1+ year experience with Kubernetes.

Aufgaben

• Develop Business Domain understanding, bridges this to technical stakeholders.
• Sets technical strategy in partnership with Data Science, Product, Architecture, and Kroger partners.
• Oversees team(s) of Engineers including operations and development, blended Offshore/Onshore.
• Provide mentoring to junior resources.
• Lead design and development of Databricks-based solutions.
• Perform development of REST APIs hosted in Kubernetes.
• Execute unit and integration testing.
• Collaborate with senior resources to ensure consistent development practices.
• Bring new perspectives to problems and be driven to improve yourself and the way things are done.

Kenntnisse

Data development

Agile/Scrum

Python

REST APIs

Kubernetes

Spark/Databricks

ETL/Data warehousing

Ausbildung

Bachelor’s degree in CS, MIS or similar

Tools

Databricks

Jobbeschreibung

Lead Data Engineer at 84.51° will design and implement solutions to ingest, store, transform, and distribute big data on Azure Cloud. You will work with Databricks (Spark, SQL, PySpark), Databricks Workflows, Python, JSON, and SQL to power customer personalization at scale.

Collaboration with Product and Data Science is critical; mentoring junior staff and leading Databricks-based projects are key expectations in this role.