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Job Description & How to Apply Below
This role combines technical leadership with hands-on development , driving best practices across data engineering initiatives and mentoring team members.
Key Responsibilities:
- Design, develop, and maintain robust ETL/ELT data pipelines using PySpark, SQL, and GCP-native services
Lead end-to-end data engineering initiatives , ensuring scalability, performance, and reliability
Build and optimize workflows using Cloud Dataflow, Dataproc, Cloud Composer, and Apache Airflow
Implement and enforce data quality, governance, security, and performance standards
Collaborate closely with product, analytics, platform, and business teams for end-to-end delivery
Mentor junior engineers and drive best practices in coding, architecture, and cloud data design
Troubleshoot complex data issues and optimize processing for large-scale datasets
Mandatory
Skills:
- Google Cloud Platform (GCP):
Strong hands-on experience with Cloud Storage for data lake implementations
Expertise in Big Query for large-scale analytics and data warehousing
Experience with Dataproc for Spark and Hadoop-based processing
Proficiency in Cloud Composer for workflow orchestration
Hands-on experience with Dataflow for batch and streaming data pipelines
Knowledge of Pub/Sub for event-driven and real-time data ingestion
Experience using Data stream for change data capture (CDC)
Familiarity with Database Migration Service (DMS) for data migrations
Exposure to Analytics Hub for data sharing and governance
Experience with Workflows for service orchestration
Working knowledge of Dataform for analytics engineering and transformations
Hands-on experience with Data Fusion for data integration
Big Data & Data Engineering:
Strong expertise in PySpark for large-scale data processing
Solid understanding of the Hadoop ecosystem
Experience designing and implementing ETL / ELT frameworks
Advanced proficiency in ANSI SQL for data transformation and analytics
Hands-on experience with Apache Airflow for pipeline scheduling and monitoring
Programming
Languages:
Proficient in Python for data engineering and automation
Working knowledge of Java for backend or big data applications
Experience with Scala for Spark-based data processing
Required Experience:
- 3–12 years of experience in Data Engineering
Strong hands-on expertise in GCP-based big data solutions
Experience leading or owning data platform or pipeline initiatives
Proven ability to design high-performance, scalable data architectures
Excellent communication and stakeholder collaboration skills
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