Data Engineer

Overview We are looking for a Senior Data Engineer who will help in implementing data analytics products using on-prem and cloud data platforms.

Position Responsibilities:

• Work with cutting-edge big data platforms (e.g., Databricks, Apache Spark) at large scale, pushing the boundaries of data processing and model enablement.
• Build and maintain robust ETL/ELT pipelines for ingestion, transformation, and aggregation of large-scale datasets on Hadoop and enterprise data platforms.
• Develop high-performance data processing jobs using PySpark/Spark, Python on data platforms such as cloudera and databricks.
• Optimize pipeline performance and cost through partitioning, file formats, compute tuning, and efficient query patterns
• Contribute to CI/CD for data workflows (testing, code reviews, deployment automation), promoting engineering best practices and maintainable codebases.
• Partner with Product Managers to develop a deep understanding of users and use cases and apply that knowledge to scoping and building new modules and features

Ideal Candidate Qualifications:

• Strong hands-on experience in data engineering building production-grade pipelines on big data platforms (Hadoop ecosystem and cloud data platforms - databricks).
• High proficiency in using Python, Spark, Hadoop platforms & tools (Hive, Impala, Airflow, NiFi), SQL to build Big Data products.
• Hands-on experience with cloud data platforms such as databricks, Client (databricks preferred)
• Experience with orchestration/integration tools such as Apache Airflow, Apache NiFi, or Talend.
• Working knowledge of DevOps/CI-CD practices: version control (Git), automated testing, release pipelines, and observability.
• Strong problem-solving skills with the ability to debug complex data issues and communicate clearly with technical and non-technical stakeholders.
• Experience developing Java based applications is an added advantage.