Career Category

Job Description

Develop, test, and maintain data pipelines using Databricks, PySpark, and Python.

Ingest, transform, and process structured and semi-structured data from multiple sources.

Support the development of scalable ETL/ELT workflows for analytics, reporting, and machine learning use cases.

Work with data engineers, analysts, and data scientists to understand data requirements and deliver reliable datasets.

Perform data cleansing, validation, and quality checks to ensure accuracy and consistency.

Optimize Spark jobs and Databricks notebooks for performance, reliability, and cost efficiency.

Create and maintain documentation for data pipelines, workflows, data definitions, and processes.

Assist in troubleshooting pipeline failures, data issues, and performance bottlenecks.

Follow best practices for version control, code quality, testing, and deployment.

Support basic AI/ML data preparation activities, including feature engineering, dataset creation, and model input preparation.

Monitor scheduled jobs and workflows to ensure timely and successful data delivery.

Collaborate with cross-functional teams in an Agile or iterative development environment.

Basic Qualifications and Experience 

2-6 years of experience with Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Engineering, Mathematics, or a related field, or equivalent practical experience

Must-Have Qualifications

Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Engineering, Mathematics, or a related field, or equivalent practical experience.

Hands-on experience with Python for data processing, scripting, and automation.

Strong working knowledge of PySpark and distributed data processing concepts.

Proven hands-on experience using Databricks for data engineering, including notebooks, clusters, jobs, workflows, Delta tables, and performance optimization.

Ability to build, maintain, and troubleshoot scalable ETL/ELT pipelines in Databricks.

Experience working with Delta Lake and lakehouse architecture concepts.

Working knowledge of SQL for querying, transforming, and validating data.

Ability to work with structured and semi-structured data formats such as CSV, JSON, Parquet, and Delta.

Understanding of data engineering concepts such as ETL/ELT, data pipelines, data lakes, data warehouses, batch processing, and data quality.

Basic understanding of AI and machine learning concepts, including features, training datasets, model inputs/outputs, and model evaluation basics.

Experience supporting data preparation or feature engineering for AI/ML use cases.

Familiarity with cloud-based data platforms, preferably  AWS, Azure, or GCP.

Understanding of Git or other version control tools.

Strong analytical, problem-solving, and troubleshooting skills.

Good communication skills and ability to work collaboratively with technical and non-technical stakeholders.

Willingness to learn new tools, technologies, and data engineering best practices.

Preferred Qualifications

Exposure to Delta Lake, Unity Catalog, or Lakehouse architecture.

Experience with workflow orchestration tools or Databricks Jobs.

Familiarity with CI/CD practices for data engineering projects.

Exposure to machine learning workflows using MLflow, scikit-learn, or similar tools.

Experience with Tableau, Power BI, or similar data visualization tools to create dashboards, support reporting needs, validate datasets, and perform exploratory analysis.

Understanding of data governance, security, and access control concepts.

Experience working in an Agile/Scrum environment.