Description
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.

Responsibilities
Required SkillsStrong Expertise

- SQL / PL/SQL
- Complex joins, CTEs, analytical functions
- Query optimization and performance tuning
- Data modeling and database concepts

- BigQuery

- Query development and optimization
- Partitioning and clustering concepts




- Data loading and transformation techniques

- ETL/Data Engineering

- Data ingestion patterns (Batch and Incremental)
- Data transformations and validations
- Data quality checks and reconciliation

Positive to Have
- Apache Airflow
- Developing and troubleshooting DAGs
- Scheduling, monitoring, and dependency management

- Python

- Basic to intermediate scripting
- Data processing and automation

- Cloud Platforms (Preferably GCP)

- Cloud Storage
- BigQuery
- Composer/Airflow

Qualifications
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
1. Advanced SQL/PLSQL
2. ETL & Data Pipeline Design
3. BigQuery Concepts
4. Data Debugging & Root Cause Analysis
5. Airflow DAG Development
6. Python Basics
7. Data Migration & Data Validation
8. 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

📌 Data Engineer (India)
🏢 Zensar Technologies
📍 India