GCP Data Engineer
New
L
LingaroData engineering
Listing location: India; Workplace type: RemoteContractSenior
Salary not disclosed
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Job Details
- Experience
- At least 8-10 years of experience as a Data Engineer working with 6+ years in GCP cloud-based infrastructure & systems.
- Required Skills
- PythonSQLApache AirflowGCPGitData modelingBigQuerydbt
Requirements
- Have 8–10 years of experience as a Data Engineer, including 6+ years working with GCP cloud-based infrastructure and systems.
- Have deep knowledge of Google Cloud Platform and cloud computing services.
- Have extensive experience designing, building, and deploying cloud data pipelines to ingest data from databases, APIs, or streaming platforms.
- Be proficient in SQL, with BigQuery required, and knowledgeable in NoSQL database management.
- Have programming skills in SQL, Python, and other scripting.
- Be proficient in data modeling, database optimization, query optimization, indexing, and performance tuning.
- Have knowledge of at least one orchestration and scheduling tool; Airflow is required.
- Have experience with data integration techniques such as ETL and ELT.
- Know modern data transformation tools such as dbt or Dataform.
- Have knowledge of Git, Jira, and Confluence.
- Be able to collaborate across teams, explain technical concepts to non-technical stakeholders, and participate in or lead client discussions.
- Be open to learning new technologies and solutions and have experience in multinational environments and distributed teams.
Responsibilities
- Design, model, and develop a client’s GCP data ecosystem, including Cloud Storage, Cloud Functions, and BigQuery.
- Gather, analyze, model, and document business and technical requirements in direct contact with clients.
- Model data from various sources and technologies.
- Design and optimize data storage architectures, including data lakes, data warehouses, and distributed file systems.
- Apply partitioning, compression, and indexing to optimize data storage and retrieval.
- Tune queries, identify bottlenecks, and implement caching strategies to improve data retrieval and system efficiency.
- Troubleshoot data processing, storage, and infrastructure issues; monitor performance, identify anomalies, and conduct root cause analysis.
- Train and mentor less experienced data engineers, providing guidance and knowledge transfer.
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