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Data Engineer

format:Hybridcompany:Outsource
google cloudbigquerypub/subgkecloud storagesnowflakeclickhousecdcetldbtairflowci/cdgithub actionspythonsql
gojavascalacomputer visionawsazureoracle cloud
semantic dedup

Reposted 2× across Djinni — deduped to one listing.

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experience4+ years
domainAI
> full description

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

  • 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).
  • 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.
  • 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.
  • 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.
     

We offer*:

  • Flexible working format - remote, office-based or flexible
  • A competitive salary and good compensation package
  • Personalized career growth
  • Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
  • Active tech communities with regular knowledge sharing
  • Education reimbursement
  • Memorable anniversary presents
  • Corporate events and team buildings
  • Other location-specific benefits

    *not applicable for freelancers