Data Engineer

Key Responsibilities Design, develop, and support batch and incremental data ingestion pipelines into the data lake. Develop and optimize complex SQL queries, data transformations, and ETL workflows. Build, maintain, and monitor Airflow DAGs for orchestration and workflow automation. Analyze and troubleshoot data quality, data reconciliation, and pipeline execution issues. Perform end-to-end data lineage analysis and trace data from source systems to downstream consumers. Debug data discrepancies by investigating source systems, transformation logic, and target datasets. Support data migration and modernization initiatives from legacy platforms to cloud-based data platforms. Work closely with business stakeholders, architects, and upstream/downstream teams to understand data requirements. Validate data completeness, accuracy, and consistency across multiple systems. Participate in production support activities and resolve data-related incidents within SLA timelines. Develop reusable data engineering components and recommend process improvements. Preferred Candidate Profile Strong analytical and problem-solving skills. Ability to independently investigate and resolve data issues. Good communication and stakeholder management skills. Experience in cloud data lake or warehouse environments. Exposure to customer, transactional, or enterprise-scale datasets is preferred. Screening Focus Areas Advanced SQL/PLSQL ETL & Data Pipeline Design BigQuery Concepts Data Debugging & Root Cause Analysis Airflow DAG Development Python Basics Data Migration & Data Validation Source-to-Target Traceability and Data Lineage Must Have: SQL/PLSQL, BigQuery/Cloud Data Warehouse, ETL, Data Analysis & Debugging Good to Have: Airflow DAG Development, Python, GCP Services Kafka, Pub/Sub optional