Parallel Score is a product development firm that develops data and user-centric solutions by leveraging designs, engineering, and innovative thinking. We are a provocative product development agency that is focused on imagining and building highly-interactive and user- driven experiences that push the limits of user design and development. We dissect where our clients are and plot a course for where they want to be using an inclusive and collaborative process integrating strategy, user experience, design, and engineering from project ideation through completion. We push the limit of what our clients think is possible. We challenge status quo. Together, we’ll come up with amazing ideas that get results.
- We are looking for a Data Engineer experienced in Databricks and cloud-native data architecture to build scalable data pipelines and optimize analytics workflows. You will ensure that data is reliable, accessible, and efficiently processed for analytical and AI initiatives.
Key Responsibilities
- Design, build, and maintain ETL/ELT pipelines using Databricks (PySpark, Delta Lake).
- Integrate and transform data from diverse structured and unstructured sources.
- Implement data quality, monitoring, and governance processes.
- Optimize data storage and compute performance in AWS, Azure, or GCP.
- Collaborate with data scientists and analysts to support advanced modeling and BI.
Required Skills & Experience
- 5+ years in data engineering or big data development.
- Hands-on expertise in Databricks, Apache Spark, and SQL.
- Strong coding ability in Python and familiarity with data orchestration tools (Airflow, dbt).
- Experience with data lakehouse architectures and CI/CD pipelines.
Preferred
- Relevant Cloud certification
- Knowledge of Delta Live Tables, streaming data (Kafka), or machine learning integration in Databricks.
Method of Application
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