Lead Data Engineer

Skill: Lead Data Engineer -GCP

Experience: 10+ Years

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:

• 10+ years of hands-on experience in Python for backend or data engineering projects.
• Strong understanding and working experience with GCP cloud services (especially Dataflow, BigQuery, Cloud Functions, Cloud Composer, etc.).
• Solid understanding of data pipeline architecture, data integration, and transformation techniques.
• Experience in working with version control systems like GitHub and knowledge of CI/CD practices.
• Experience in Apache Spark, Kafka, Redis, Fast APIs, Airflow, GCP Composer DAGs.
• Strong experience in SQL with at least one enterprise database (SQL Server, Oracle, PostgreSQL, etc.).
• Experience in data migrations from on-premise data sources to Cloud platforms.

Good to Have (Optional Skills):

• Experience working with the Snowflake cloud data platform.
• Hands-on knowledge of Databricks for big data processing and analytics.
• Familiarity with Azure Data Factory (ADF) and other Azure data engineering tools.