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Software Engineer, Enterprise Data Platform

Ingénieur·e logiciel, plateforme de données d'entreprise

Remote (EST timezone) • Remote • Full-time Contract • Req ID: GDBI-2L4N6I0K

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Software Engineer, Enterprise Data Platform

Location: Remote (EST preferred) • Type: Full-time • Status: Open
About Global Data and BI Global Data and BI is hiring on a rolling basis for Data, BI & Analytics and ML/AI projects for large corporations. Beyond consulting services, we build production-ready business platforms and SaaS applications that leverage modern cloud technologies, data analytics, and AI capabilities. We deliver enterprise-grade solutions with certified engineers in AWS and Microsoft Azure / Power BI technologies. Our focus includes transformation projects in the financial industry, custom platform development, and our proprietary Noème suite of products. Our mission is to transform the corporate data journey from complexity to strategic clarity, ensuring data is not just collected, but leveraged to drive smarter decisions, stronger businesses, and lasting impact.

Summary

Join Global Data and BI's Enterprise Data Platform team as we build secure, compliant, and scalable data infrastructure for Fortune 500 clients in highly regulated industries. You'll help design and build enterprise-grade data platforms that power AI, analytics, and business intelligence while meeting stringent security, privacy, and compliance requirements. This role focuses on the data platform layer (including lakehouse architecture, data governance, encryption, access control, and multi-region data architectures) and partners closely with Security, Client Success, Data Engineering, and AI teams.

What you'll do (roles & responsibilities)

  • Design and evolve modern data lakehouse architectures for enterprise clients, implementing table formats (Apache Iceberg, Hudi, Delta Lake) that serve as the source of truth for analytics, AI, and business intelligence
  • Build and operate core lakehouse components including data catalogs, schema management, metadata tracking, and data lineage systems that enable self-service analytics and governance
  • Design, implement, and harden batch and streaming data pipelines using Spark, Kafka, Databricks, Azure Data Factory, and AWS services that move and transform data reliably across regions, cloud providers, and client environments
  • Work with Security and compliance teams to integrate Enterprise Key Management (EKM), implement file- and record-level encryption, and ensure safe key handling in distributed processing systems
  • Build primitives for fine-grained access control and data governance, enabling role-based access, attribute-based policies, and comprehensive audit logging so clients can track who accessed what data, when, and under which guarantees
  • Implement data residency and sovereignty requirements for global clients, ensuring data stays within specified geographic boundaries and meets regional compliance requirements
  • Raise the operational bar for enterprise data platforms by improving monitoring, debugging, alerting, and incident response for data pipelines and services
  • Tackle performance and cost optimization challenges across Kafka, Spark, cloud storage, and compute for very large enterprise workloads (thousands of users, petabyte-scale data, multi-region deployments)
  • Build infrastructure to support AI/ML workflows including training pipelines, feature stores, model serving, and embedding infrastructure on top of shared data platforms
  • Design and implement multi-region and multi-cloud data architectures that support disaster recovery, high availability, and compliance with data localization requirements
  • Partner with Data Engineering teams to build reusable data transformation frameworks, data quality checks, and orchestration patterns
  • Contribute to technical architecture decisions, design documents, and platform evaluations that influence long-term infrastructure direction and technology choices
  • Ensure compliance with regulatory requirements including SOC 2, GDPR, CCPA, HIPAA, and industry-specific regulations for financial services clients
  • Build observability and monitoring infrastructure for data platforms using modern tools for metrics, logging, tracing, and anomaly detection
  • Collaborate with enterprise clients to understand their security, governance, and compliance requirements and deliver platform capabilities that meet their needs
  • Mentor junior engineers and establish best practices for building secure, scalable data platforms

What you should have (Must-Haves)

  • Bachelor's or Master's degree in Computer Science, Software Engineering, or equivalent practical experience
  • 5+ years of experience building and operating data platforms or large-scale data infrastructure for enterprise SaaS or similar environments
  • Strong programming skills in Python, Java, or Scala; comfortable working with SQL for analytics and complex data modeling
  • Deep hands-on experience with distributed data processing systems (Apache Spark, Databricks, AWS EMR, Azure Synapse) including debugging, performance tuning, and optimization
  • Streaming and data ingestion expertise with Kafka, Azure Event Hubs, AWS Kinesis, or equivalent systems; experience with CDC patterns and data replication (Debezium, Fivetran, custom connectors)
  • Lakehouse and modern data architecture experience with table formats like Apache Iceberg, Hudi, or Delta Lake, and understanding of data catalogs and schema evolution
  • Security and data governance expertise: Practical understanding of encryption (at rest and in transit), access control patterns, key management, audit logging, and compliance requirements
  • Cloud infrastructure experience with AWS and/or Azure, including managed data services (EMR, Glue, Databricks, Azure Data Factory, Synapse Analytics, S3, ADLS, Redshift, Snowflake)
  • Production operations experience: Comfortable owning services and pipelines in production, including on-call rotations, incident response, post-mortems, and reliability improvements
  • Strong problem-solving and systems thinking: Ability to design complex distributed systems that balance performance, cost, security, and operational simplicity
  • Experience with Infrastructure as Code (Terraform, Pulumi, CloudFormation, ARM templates) for managing data infrastructure
  • Understanding of data compliance requirements (SOC 2, GDPR, CCPA) and how they impact platform architecture
  • Excellent communication skills: Ability to work with cross-functional teams, explain technical decisions, and collaborate with enterprise clients

Nice-to-Haves

  • Direct experience working with enterprise customers on features like data residency, sovereign cloud, EKM, CMEK, or compliance-driven auditing
  • Prior work on enterprise data governance platforms like Databricks Unity Catalog, AWS Lake Formation, Azure Purview, or similar catalog/governance systems
  • Experience implementing multi-region and multi-cloud data architectures with cross-region replication, disaster recovery, and data locality requirements
  • Background in financial services or other highly regulated industries with complex compliance requirements
  • Experience building ML/AI infrastructure including training/evaluation workflows, feature stores, model registries, and MLOps pipelines on shared data platforms
  • Familiarity with vector databases (Pinecone, Milvus, pgvector) or search infrastructure (Elasticsearch, OpenSearch) and their integration with upstream data systems
  • Experience with modern data stack tools (dbt, Great Expectations, Monte Carlo, Airbyte) and understanding of data quality frameworks
  • Expertise in data observability and monitoring platforms (Datadog, Honeycomb, OpenTelemetry) with experience implementing metrics, traces, and log aggregation for data systems
  • Experience with Kubernetes or containerized data workloads and orchestration
  • Knowledge of Power BI, Tableau architecture and integration with enterprise data platforms
  • Understanding of data mesh or federated data architecture patterns
  • Experience with zero-trust security models and their application to data platforms
  • Contributions to open source data infrastructure projects (Spark, Iceberg, Kafka, dbt, etc.)
  • Bilingual proficiency in English and French (asset for Quebec clients and bilingual platform requirements)
  • Certifications in cloud platforms (AWS Solutions Architect Professional, Azure Solutions Architect Expert) or security (CISSP, CISM)

Work details

  • Location: Remote (EST timezone preferred for team collaboration and client engagement)
  • Reporting relationship: You will report to the Senior Engineering Manager or Director of Data Platform Engineering

Compensation

Competitive senior-level salary package adjusted to your local market and country of residence. Compensation is location-based and reflects regional market standards. We benchmark against leading technology companies in your country to ensure competitive, fair pay for senior/staff-level engineering talent with enterprise data platform expertise.

Our commitment to diversity, equity, and inclusion We're committed to employment equity and encourage women, Indigenous Peoples, persons with disabilities, veterans and persons of all races, ethnicities, religions, abilities, sexual orientations, and gender identities and expressions to apply. We also welcome applications from Latin America countries, as the position is remote.

What happens after you apply

Timeline: We aim to complete our process within 2-3 weeks of application.

Step 1
Apply Submit your application. If you applied before and were not successful, you are welcome to apply again.
Step 2
Initial call (1 hour) If pre-selected, we schedule an initial conversation.
Step 3
Technical interview (2 hours) If shortlisted, deep dive on system design, data architecture, and security with 2-3 senior engineers.
Step 4
Panel interview (1h30) Meet with Engineering Leadership and Product teams.
Step 5
Hiring decision If selected, we send an offer and next steps, otherwise we inform you by email.
If you don't hear back from us after application or any interview stage, it means we do not pursue your application.
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