GCP data engineer

Posted on June 12, 2025

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Job Description

  • Key Responsibilities
  • Act as a subject matter expert in GCP data technologies, providing strategic and technical leadership across mission-critical banking and financial data projects.
  • Collaborate with cross-functional teams�including business stakeholders, data scientists, and ML engineers�to design robust, scalable, and secure data architectures that support both analytical and ML-driven use cases.
  • Build and optimize data pipelines using GCP services such as BigQuery, Dataflow, Apache Beam, Cloud Composer (Airflow), Cloud Storage, and Dataproc, ensuring data readiness for real-time analytics and ML applications.
  • Support MLOps workflows by provisioning and transforming data for ML feature stores, model training, and real-time inference pipelines, in collaboration with ML engineering teams.
  • Apply domain knowledge to deliver secure and compliant data solutions that meet banking standards for regulatory reporting, fraud detection, risk analytics, and customer intelligence.
  • Lead performance tuning and cost optimization efforts�especially for BigQuery workloads�to maximize efficiency when processing large-scale financial datasets.
  • Ensure production-grade data quality, lineage, and governance by integrating metadata management, validation frameworks, and audit-readiness practices required in the banking domain.
  • Work within Agile teams to rapidly prototype data products and iterate based on feedback, balancing innovation with operational rigor.
  • Enable business insight through collaboration with BI teams and use of visualization tools such as Power BI or Looker, delivering dashboards that support compliance, operations, finance, and customer experience.
  • Oversee the end-to-end data lifecycle�from design and development to deployment, monitoring, and optimization�while also accommodating non-functional requirements such as reliability, observability, and scalability.
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  • Required Skills & Qualifications
  • 6�10 years of data engineering experience, including 3+ years on GCP delivering production-scale solutions.
  • Proficiency in GCP services: BigQuery, Dataflow, Apache Beam, Cloud Composer (Airflow), Dataproc, and Cloud Storage.
  • Experience designing secure, compliant, and high-performing data pipelines in a regulated industry, preferably banking or financial services.
  • Solid understanding of data security and privacy principles (e.g., IAM, VPC SC, encryption, data classification, and access control in BigQuery).
  • Proven ability to support ML workflows and pipelines, including data preparation for feature stores, model training data pipelines, and model serving integration.
  • Strong programming background in SQL and Python, with familiarity in Java as a plus.
  • Proficient in DevOps and infrastructure-as-code practices, including tools such as Terraform, Git, and CI/CD frameworks.
  • Hands-on experience with data governance, quality, and lineage tools.
  • Excellent communication skills and stakeholder engagement experience, particularly in working with finance, risk, compliance, and analytics teams.
  • Experience working in Agile teams and delivering data solutions in high-compliance, fast-paced environments.
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  • Preferred Qualifications
  • GCP Professional Data Engineer or Cloud Architect certification.
  • Experience in MLOps tooling (e.g., Vertex AI, Kubeflow, TFX) or orchestration of ML pipelines in production environments.
  • Familiarity with banking-specific data domains such as KYC/AML, credit risk modeling, fraud analytics, or regulatory reporting.
  • Exposure to real-time streaming, pub/sub messaging systems, and low-latency inference use cases.

Required Skills

gcp services: bigquery dataflow apache beam cloud composer (airflow) dataproc and cloud storage.