Role: Data Engineer
Location: NYC
Role description
Key Responsibilities
Architect and own scalable, secure, cloud-native data platforms on Google Cloud Platform Design, build, and optimize batch and real-time data pipelines using BigQuery, Dataflow, Pub/Sub, and Dataproc Lead BigQuery performance tuning and cost optimization (partitioning, clustering, query efficiency)
Orchestrate workflows using Cloud Composer (Apache Airflow)
Enable Al/ML and GenAl integration via Vertex Al and BigQuery ML
Enforce data governance, security, reliability, and FinOps best practices
Mentor engineers, conduct design/code reviews, and set enterprise data engineering standards
Collaborate with product, analytics, and data science teams to deliver business-critical insights
Key Skill Sets
Google Cloud Platform Data Services: BigQuery, Dataflow (Apache Beam), Pub/Sub, Cloud Storage, Cloud Composer, Dataproc
Programming & SQL: Advanced SQL, Python (Java/Scala a plus)
Data Engineering: ETL/ELT, streaming & batch processing, data modeling, distributed systems
Modern Architectures: Lakehouse, Apache Iceberg, Data Mesh concepts
Al/ML Enablement: Vertex Al, BigQuery ML, GenAl-ready pipelines DevOps & laC: Terraform, CI/CD, DataOps practices
Leadership: Architecture ownership, mentoring, stakeholder communication, problem solving
Certification: Google Cloud Professional Data Engineer (strongly preferred / often mandatory)
In addition to big query, storage bucket, following are necessary skills - data flow, composer, cloud scheduler, Pubsub and Kafka, Apigee gateway and API, Dataplex, basic knowledge of network connectivity (knowledge on data catalog, DLP, BQDTS, STS and other data transfer methodologies). Reporting background (powerbi) and ICEBERG are MUST. Data virtualization (Trenio or equivalent), Looker and Google Cloud Platform vertex will be a plus.
Position 4
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