Middle Data Engineer (Data, ML, BI & Automation) — BigQuery & Looker

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About Us

Kyriba is a global leader in liquidity performance that empowers CFOs Treasurers and IT leaders to connect protect forecast and optimize their liquidity. As a secure and scalable SaaS solution Kyriba brings intelligence and financial automation that enables companies and banks of all sizes to improve their financial performance and increase operational efficiency. Kyribas real-time data and AI-empowered tools empower its 3000 customers worldwide to quantify exposures project cash and liquidity and take action to protect balance sheets income statements and cash flows. Kyriba manages more than 3.5 billion bank transactions and $15 trillion in payments annually and gives customers complete visibility and actionability so they can optimize and fully harness liquidity across the enterprise and outperform their business strategy. For more information visit.

We are seeking a versatile and innovative Data Engineer to design build and maintain scalable data pipelines and infrastructure that support analytics reporting Machine Learning (ML) Generative AI (GenAI) Business Intelligence (BI) and automation initiatives. The ideal candidate will have practical experience with Google Cloud BigQuery and modern data processing with a keen interest in enabling advanced analytics and automation across the organization.

Key Responsibilities

Data Engineering

• Design implement and optimize robust ELT/ETL pipelines using Google BigQuery Cloud Storage and GCP services (e.g. Dataflow Pub/Sub Cloud Composer) to support analytics ML BI and automation use cases.

• Build and maintain data architectures for structured and unstructured data ensuring data quality lineage and security.

• Integrate data from multiple sources including external APIs and on-premise systems to create a unified well-modeled data environment.

• Apply BigQuery best practices including partitioning clustering materialized views and cost/performance optimization.

Machine Learning & GenAI

• Collaborate with Data Scientists and ML Engineers to deliver datasets and features for model training validation and inference.

• Develop and operationalize ML/GenAI pipelines automating data preprocessing feature engineering model deployment and monitoring using Vertex AI and/or BigQuery ML.

• Support the deployment and maintenance of GenAI models and LLMs in production environments including prompt/feature pipelines and inference orchestration.

• Stay current on emerging ML and GenAI technologies and best practices across the GCP ecosystem.

Business Intelligence & Reporting

• Partner with BI Developers and Analysts to provide clean reliable governed data sources for reporting and dashboarding in Looker (semantic modeling in LookML).

• Enable data access and transformation for self-service BI; ensure BI solutions are scalable secure and performant.

• Integrate advanced analytics and ML/GenAI outputs into BI datasets Looks and Explores for actionable insights.

Automation

• Partner with Automation Specialists to design and implement data-driven automated workflows using MuleSoft and/or GCP services (e.g. Cloud Functions Workflows Cloud Run).

• Develop and maintain automation scripts and integrations to streamline data flows improve operational efficiency and reduce manual effort.

Governance & Collaboration

• Implement data governance security and compliance best practices across all data assets leveraging tools such as Dataplex and Data Catalog for lineage and metadata.

• Document data flows pipelines and architectures for technical and business stakeholders.

• Collaborate across teams (data science BI business IT) to align data engineering efforts with strategic objectives and SLAs.

Requirements

• Bachelors or Masters degree in Computer Science Engineering Information Systems or related field.

• Proven experience as a Data Engineer or similar role.

• Expertise with Google BigQuery and Google Cloud Storage; solid knowledge of GCP data and streaming services (Dataflow/Apache Beam Pub/Sub Cloud Composer/Airflow).

• Strong programming skills in Python and SQL

• Experience building reliable data pipelines for analytics ML BI and automation use cases.

• Familiarity with ML frameworks (scikit-learn TensorFlow PyTorch) MLOps on GCP (Vertex AI Pipelines/Model Registry) or BigQuery ML and GenAI libraries/tooling where applicable.

• Experience supporting BI/reporting solutions preferably with Looker and LookML.

• Hands-on experience with automation/integration platforms such as MuleSoft is a strong plus.

• Understanding of data governance security quality and compliance on cloud platforms.

• Excellent communication collaboration and problem-solving skills.

Nice to Have

• Experience deploying and operationalizing GenAI/LLM solutions at scale on GCP (Vertex AI vector search embeddings).

• Experience with API development and integration (Cloud Run/Functions Apigee).

• Knowledge of DevOps/CI-CD for data solutions (Cloud Build Git Infrastructure as Code such as Terraform).

• Relevant Google Cloud or Looker certifications (e.g. Professional Data Engineer LookML Developer).