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.

ResponsibilitiesRequired 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

Good 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

QualificationsPreferred 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

About UsAt Zensar, we’re “experience-led everything”. We are committed to conceptualizing, designing, engineering, marketing, and managing digital solutions and experiences for over 130 leading enterprises. We are a company driven by a bold purpose: Together, we shape experiences for better futures. Whether for our clients, our people, or the world around us, this belief powers everything we do. At the heart of our culture is ONE with Client - a set of four core values that reflect who we are and how we work: One Zensar, Nurturing, Empowering, and Client Focus.
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We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.