Roles & Responsibilities :

- 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 Bachelors degree in Computer Science, Data Engineering, Information Systems, Engineering, Mathematics, or a related field, or equivalent practical experience.

Must-Have Qualifications :

- Bachelors 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.

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