Junior Data Engineer (Part Time)
Summary
Build and maintain data pipelines in Databricks using Python, Spark, and SQL to process retail transaction data and power AI-driven marketing tools for Swiftly’s retail clients.
Swiftly is the AI platform purpose-built for retail, serving retailers, wholesalers, and brands. Swiftly's AI agents unify data, automate workflows, and power the marketing that drives measurable business growth. Swiftly AI is built on the industry's largest proprietary retail dataset: live transactions, 9 million products, and the closed-loop campaign outcomes of 33,000 stores.
Trusted by leading retailers, wholesalers, and hundreds of iconic brands, Swiftly unifies data across the retail ecosystem to accelerate growth and power the connected, personalized experiences that drive lasting shopper loyalty.
Swiftly is searching for a Junior Data Engineer to help scale our Retailer Platform that spans multiple technologies using JVM stack (e.g., Java, Scala, Kotlin) and SQL (any variant). This role will apply statistical and analytical skills and data engineering fundamentals to support building high-quality data products. The ideal candidate will be able to prioritize observability in designs, ensuring solutions are equipped with comprehensive monitoring, logging, and alerting to facilitate proactive issue detection and resolution.
This is a part-time, short-term contract role for a 2-month engagement. Availability of 20 hours per week is required. We use Deel as the contract of record.
Responsibilities Include:
- Taking customer data and processing it via Python and Spark in a Databricks environment using SQL in combination
- Working with Data Analysts to answer data questions and offload repeatable processes
- Validating data processes and results
- Performance tuning of big data batch processing and streaming pipelines
- Creating dashboards & alerts for internal process and data monitoring, using Databricks, Grafana and Tableau
- Other related duties as assigned
Required Qualifications:
- 1+ years of industry experience architecting, coding, verifying and operating large-scale data pipelines and architectures
- 1+ years of professional experience with Spark, Python and SQL
- Demonstrated ability to work collaboratively in an ambiguous, fast-paced environment
- Experience with NoSQL, Document DB, Key-Value, Columnar and blob data storage patterns
- History working with multiple vendor solutions for repository patterns and the ability to quickly identify the right solution for a given data architecture and workload
- Proven ability to collaborate with client and service engineering teams to ensure the best possible data architecture
- Operational experience working with large scale data warehouses and/or data lakes, experience with different distributed processing frameworks that can handle data in batch
- Build data products that are well-modeled, documented, and easy to understand and maintain
- Availability of 20 hours per week is required
Preferred Qualifications:
- Understanding of data science and have experience building data pipelines to support data scientists
- Experience with ad-hoc analysis and data visualization platforms including Excel, PowerBI, and Tableau
- Familiarity with common data engineering scripting languages