Lead Data Pipeline Architect

Job Overview :

We are looking for a skilled and motivated Lead Data Engineer with strong experience in Python programming and Google Cloud Platform (GCP) to join our data engineering team. The ideal candidate will be responsible for requirements gathering, designing, architecting the solution, developing, and maintaining robust and scalable ETL (Extract, Transform, Load) & ELT data pipelines. The role involves working with customers directly, gathering requirements, discovery phase, designing, architecting the solution, using various GCP services, implementing data transformations, data ingestion, data quality, and consistency across systems, and post post-delivery support.

Experience Level : 10 to 12 years of relevant IT experience

Key Responsibilities :

• Design, develop, test, and maintain scalable ETL data pipelines using Python.
• Architect the enterprise solutions with various technologies like Kafka, multi-cloud services, auto-scaling using GKE, Load balancers, APIGEE proxy API management, DBT, using LLMs as needed in the solution, redaction of sensitive information, DLP (Data Loss Prevention) etc.
• Work extensively on Google Cloud Platform (GCP) services such as :
• Dataflow for real-time and batch data processing
• Cloud Functions for lightweight serverless compute
• BigQuery for data warehousing and analytics
• Cloud Composer for orchestration of data workflows (on Apache Airflow)
• Google Cloud Storage (GCS) for managing data at scale
• IAM for access control and security
• Cloud Run for containerized applications
• Should have experience in the following areas :
• API framework : Python FastAPI
• Processing engine : Apache Spark
• Messaging and streaming data processing : Kafka
• Storage : MongoDB, Redis / Bigtable
• Orchestration : Airflow
• Experience in deployments in GKE, Cloud Run.
• Perform data ingestion from various sources and apply transformation and cleansing logic to ensure high-quality data delivery.
• Implement and enforce data quality checks, validation rules, and monitoring.
• Collaborate with data scientists, analysts, and other engineering teams to understand data needs and deliver efficient data solutions.
• Manage version control using GitHub and participate in CI / CD pipeline deployments for data projects.
• Write complex SQL queries for data extraction and validation from relational databases such as SQL Server, Oracle, or PostgreSQL.
• Document pipeline designs, data flow diagrams, and operational support procedures.

Required Skills :

Good to Have (Optional Skills) :

Additional Details :

Education :