Data Engineer

Data Engineer

Location: Remote

Duration: Long-term

MUST HAVE: SQL, Python, PySpark and AWS.

Nice to have Databricks experience.

Secondary Skills - Nice to Haves, Big Data, Cloud, Databricks

Job Description

Nike has embraced big data technologies to enable data-driven decisions. As a Senior Data Engineer, you will work with a variety of talented Nike teammates building first-class solutions for Product Innovation and Consumer Creation (PICC) Business Intelligence. The ideal candidate will have a passion for data, a strong grasp of data engineering paradigms and bring an engineering experience that will help innovate and improve how we build our solutions.

• Design and build cloud-based data pipelines, reusable components, frameworks, and libraries at scale to support data and BI products.

• Identify and tackle issues concerning data management to improve data quality.

• Clean, prepare and optimize data for ingestion and consumption.

• Collaborate on the implementation of new data management features and re-structure of the current data architecture.

• Build continuous integration and production deployment pipelines.

• Collaboratively review design, code, test plans and implementation performed by other data engineers on the team in support of maintaining data engineering standards.

• Analyze and profile data for designing scalable solutions.

• Troubleshoot data issues and perform root cause analysis to proactively resolve product and operational issues.

Additional Skills & Qualifications

Skills & Qualifications

• Bachelor’s degree in computer science, Information Systems, or related technical subject area.

• 5+ years of experience developing Data & Analytic solutions.

• 4+ years of experience using Python and SQL.

• 4+ years of experience building data pipelines and data lake solutions using Apache Spark/ PySpark leveraging one or more of the following: Databricks, AWS EMR, AWS Glue.

• Experience in cloud environments, preferably AWS.

• Experience building near real-time streaming data pipelines.

• Strong understanding of Data Warehouse and Lakehouse concepts, ETL/ ELT and corresponding technical architectures and infrastructure components.

• Understanding of the Business Intelligence and reporting tools landscape and solutions.

• Proficient in source control, modern build strategies, Continuous Integration, unit testing, and automated integration tests.

• Ability to define solutions, provide estimates on effort and risk, and evaluate technical feasibility.

• Ability to influence and communicate effectively with team members and business stakeholders, both verbally and in written form.