Description

Technical/Functional Skills • 5–6 years of total experience in data engineering or big data development. • 2–3 years hands-on experience with Databricks and Apache Spark. • Proficient in AWS cloud services (S3, Glue, Lambda, EMR, Redshift, CloudWatch, IAM). • Strong programming skills in PySpark, Python, and optionally Scala. • Solid understanding of data lakes, lakehouses, and Delta Lake concepts. • Experience in SQL development and performance tuning. • Familiarity with Airflow, dbt, or similar orchestration tools is a plus. • Experience in CI/CD tools like Jenkins, GitHub Actions, or Code Pipeline. • Knowledge of data security, governance, and compliance frameworks.

Experience Required 5-6 years+

Roles & Responsibilities • Develop and maintain scalable data pipelines using Apache Spark on Databricks. • Build end-to-end ETL/ELT pipelines on AWS using services like S3, Glue, Lambda, EMR, and Step Functions. • Collaborate with data scientists, analysts, and business stakeholders to deliver high-quality data solutions. • Design and implement data models, schemas, and Lakehouse architecture in Databricks. • Optimize and tune Spark jobs for performance and cost-efficiency. • Integrate data from multiple structured and unstructured data sources. • Monitor and manage data workflows, ensuring data quality, consistency, and security. • Follow best practices in CI/CD, code versioning (Git), and DevOps practices for data applications. • Write clean, reusable, well-documented code using Python / PySpark / Scala

Key Skills

Education

Any Graduate