Designs and leads scalable data solutions on GCP, including data architectures, ETL/ELT pipelines, data lakes, lakehouses, warehouses, and batch or real-time processing. Develops data models, orchestration, monitoring, governance, and production deployments while collaborating with architects, engineers, analysts, DevOps teams, and customers. Requires strong Python and SQL skills, GCP expertise, cloud data warehouse experience, and familiarity with visualization, transformation, CI/CD, and infrastructure practices.
Description
We are looking for an experienced Senior Data Engineer to join a growing Data Engineering and Analytics department. In this role, you will design and develop advanced data solutions for complex customer environments, with a strong focus on Google Cloud Platform and modern GCP data technologies. You will work across the full data lifecycle- from architecture and data modeling to pipeline development, data platforms, analytics, and production deployment.
Key Responsibilities:
- Design and develop scalable data solutions on GCP.
- Lead the technical design and implementation of customer data projects.
- Understand business and technical requirements and translate them into effective data architectures.
- Build and maintain ETL/ELT pipelines, Data Lakes, Lakehouses, and cloud-based Data Warehouses.
- Design data models and integration processes for Batch and real-time workloads.
- Work with structured, semi-structured, and unstructured data.
- Select the appropriate technologies based on performance, scalability, security, and cost requirements.
- Implement data quality, monitoring, governance, and orchestration processes.
- Work closely with Data Architects, Data Engineers, DevOps teams, analysts, and customer stakeholders.
- Participate in the development of analytics, AI, and ML solutions where relevant.
Requirements:
- At least 5 years of professional experience as a Data Engineer – mandatory.
- Proven hands-on experience developing data solutions on GCP – mandatory.
- Experience with data visualization tools such as Looker, Power BI, Tableau, or QuickSight.
- Experience with data modeling, orchestration, performance optimization, and large-scale data processing.
- Strong Python development skills, including building data pipelines and ETL/ELT processes.
- High proficiency in SQL – mandatory.
- Experience designing and developing cloud-based Data Warehouses and Lakehouse solutions.
- Experience with ETL/ELT and transformation tools such as dbt, Dataform, Rivery, or similar platforms.
- Familiarity with CI/CD, Git, Infrastructure as Code, and production deployment practices.
- Strong analytical and problem-solving skills with excellent attention to detail.
- Ability to learn new technologies independently and work across multiple projects.
- Strong experience with several of the following GCP services: BigQuery, Cloud Storage, Dataflow, Dataproc, Cloud Composer, Cloud Run or Cloud Functions
- Fluent English.
Advantages
- Hands-on experience with AWS or Microsoft Azure data services.
- Experience with services such as AWS Glue, Redshift, EMR, Kinesis, Azure Data Factory, Synapse, or Databricks.
- Experience with real-time data processing and streaming architectures.
- Experience with Kafka or other event-driven platforms.
- Knowledge of AI and ML services such as Vertex AI, Gemini, BigQuery ML, SageMaker, Bedrock, or Azure Machine Learning.
- Relevant GCP professional certifications.
- Previous experience working in consulting or customer-facing technology projects.
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