Senior Data Engineer

Location: European Union

Department: Shutterstock: Content Analytics Team

Our client is looking for a Senior Data Engineer to design, scale, and maintain high-performance ingestion, Change Data Capture (CDC), and delivery pipelines. In this role, you will architect enterprise-grade data platforms to process large-scale metadata, algorithmic feature extraction, and licensable asset datasets.

This team sits at the foundation of the data processing and feature enrichment pipelines. Driving consistency and performance across an ever-growing data lake, we provide guardrails and sound system design patterns to inform the creation and fine-tuning of the industry's cutting-edge foundational large language models.

You will leverage Google Cloud Platform (GCP) as the primary compute foundation of a multi-cloud data processing ecosystem, alongside hybrid data warehouses and query engines including Snowflake and ClickHouse to provide low-latency data exposure, incremental delta tracking, and seamless integration endpoints for downstream AI/ML consumption.

Primary Tech Stack: GCP, ClickHouse, Snowflake, dbt Core, Apache Airflow, Python, SQL.

Responsibilities
1. Ingestion Architecture & GCP Foundation:
- Architect, build, and optimize scalable job creation, batching, and high-throughput ingestion pipelines hosted on GCP.
- Implement serverless and containerized data pipelines using GCP infrastructure (BigQuery, Dataflow, Pub/Sub, Cloud Storage, GKE).
2. Enterprise ETL, CDC & Incremental Processing:
- Design robust Change Data Capture (CDC) and incremental update workflows to enable fast delta synchronization across platforms.
- Build dbt transformation models and Airflow orchestrations following Data Vault / dimensional modeling patterns.
- Implement delta tracking methods (e.g., hash keys, hash diffs) to track row-level asset modifications and metadata events.
3. Multi-Engine Analytics & Query Optimization:
- Maintain and optimize Snowflake integration layers, handling deduplication logic, canonical content filtering, and policy restrictions.
- Deploy and manage ClickHouse as a high-concurrency analytical engine to deliver sub-second metadata retrieval and indexing.
- Construct dedicated integration views and API query layers allowing internal and external clients to consume real-time delta updates.
4. Data Quality, Governance & Scalability:
- Enforce metadata schema standardization, canonical asset mapping, and automated validation layers across diverse asset types (images, video, 3D models, audio).
- Collaborate closely with cross-functional teams to integrate metadata pipelines with feature extraction tools, search indexes, and licensing platforms.

Requirements
- Google Cloud Platform (GCP): 4+ years of production experience with core GCP data technologies (BigQuery, Dataflow, Cloud Pub/Sub, GKE, Google Cloud Storage).
- Database & Query Engines: Deep technical proficiency with both Snowflake (warehousing, semi-structured queries, integration layers) and ClickHouse (real-time aggregation, low-latency columnar storage, indexing).
- Enterprise ETL / CDC: Proven experience architecting CDC pipelines and incremental delta loading for multi-terabyte dataset scale.
- Data Transformation & Orchestration: Hands-on mastery of dbt Core/Cloud and Apache Airflow in an enterprise CI/CD setting (e.g., GitHub Actions).
- Programming Skills: Advanced skills in Python and SQL; familiarity with Go, Java, or Scala is a strong plus.

Nice-to-Have Qualifications:
- Experience with Data Vault 2.0 architecture, hash-key generation, and raw/staging/integration layer design.
- Background handling AI/ML metadata, computer vision feature vectors, C2PA provenance headers, or semantic search configurations.
- Familiarity with media metadata schemas (images, 3D model stems, video derivatives).
- Experience with other major cloud providers such as AWS, Azure, or Oracle Cloud.

Key Success Metrics (First 90 Days)
- GCP Pipeline Optimization: Standardize ingestion batching and pipeline compute infrastructure on GCP.
- ClickHouse & Snowflake Integration: Establish streamlined integration views exposing low-latency metadata delta changes.
- Automated CDC Workflows: Finalize automated dbt delta transformations with hash tracking across primary warehouse layers.