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:
10 to 12 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.
Additional Details:
Excellent problem-solving and analytical skills.
Strong communication skills and ability to collaborate in a team environment.
Education:
Bachelor's degree in Computer Science, a related field, or equivalent experience.
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