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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and implementing data architectures, including data lakes and MLOps pipelines, while effectively bridging technical and business teams. Proficient in cloud ecosystems and data governance frameworks, with strong communication skills to convey complex technical concepts.
Highest-signal resume keywords
Data Architecture DesignMLOps Pipeline ArchitectureCloud Ecosystem DeploymentSQL and Python ProficiencyData Governance Frameworks
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data LakesLakehousesData WarehousesSQLPythonSparkMLflowDockerKubernetesGit
Soft Skills
Excellent CommunicationProblem AnalysisCollaborationMentoringAttention to Detail
Tools & Technologies
AzureAWSGCPDatabricksSnowflakeBigQueryKafkaAirflowPower BITableau
Industry Keywords
Data GovernanceBig Data EcosystemsStreaming ArchitecturesBI PlatformsConsulting Environment
Tech Stack
Tools & technologiesAirflowAmazon RedshiftAWSAzureBigQueryCloudDockerGoogle Cloud PlatformHadoopKafkaKubernetesPySparkPythonScalaSnowflakeSparkSQLTableau
About the role
Key responsibilities & impact- Design and shape the technical foundation of Infomineo’s Analytics & AI practice
- Lead the technical architecture of Data, Analytics, and AI solutions for clients from design through deployment
- Design end-to-end data architectures including data lakes, lakehouses, warehouses, and streaming pipelines
- Define standards for data modeling, storage, ingestion, and transformation across client engagements
- Architect MLOps and AI deployment infrastructure, including model registries, ML CI/CD, and monitoring
- Lead technical decisions on Azure, AWS, GCP, and open-source tooling
- Define best practices and reusable frameworks for data engineers, analysts, and data scientists
- Mentor and review cross-functional project teams
- Bridge data analysts, data engineers, and AI/ML engineers on complex projects
- Contribute to the internal knowledge base, toolkits, and delivery accelerators
- Lead architecture workshops and discovery sessions with client stakeholders
- Translate business requirements into scalable, robust technical blueprints
- Present architecture decisions to technical teams and executive audiences
- Support pre-sales and proposal efforts through technical scoping and solution design
- Provide internal training and knowledge-sharing sessions
- Support the Head of Practice with business development and internal capability initiatives
Requirements
What you’ll need- Master’s degree in Computer Science, Data Engineering, Software Engineering, Applied Mathematics, or a related field
- Full proficiency in English plus one additional language (French, Arabic, Spanish, German, etc.)
- 6+ years of technical experience in data architecture or a closely related field
- Proven track record in a consulting or multi-client services environment
- Proven hands-on experience designing large-scale data platforms: data lakes, lakehouses, or warehouse architectures (Databricks, Snowflake, BigQuery, Azure Synapse, Redshift)
- Strong command of SQL and at least one of Python, Scala, or Spark for data processing and transformation
- Experience with Big Data ecosystems: Hadoop, Spark, PySpark, Hive, or equivalent
- Familiarity with streaming and real-time architectures (Kafka, Flink, Spark Streaming)
- Proven hands-on experience with ML lifecycle tooling: MLflow, Kubeflow, SageMaker, Azure ML, or equivalent
- Experience architecting MLOps pipelines: model versioning, CI/CD for ML, monitoring, and drift detection
- Exposure to GenAI and LLM integration patterns (RAG architectures, vector databases, prompt pipelines)
- Proven hands-on experience with orchestration and transformation tools: Airflow, dbt, or equivalent
- Proven hands-on experience with container technologies: Docker, Kubernetes
- Proven hands-on experience with versioning software: Git, GitHub, GitLab
- Proven hands-on experience deploying solutions in cloud ecosystems: AWS, Azure, or Google Cloud
- Knowledge of data governance frameworks: data catalogs, lineage tracking, access control, and data quality management
- Exposure to BI and data visualization platforms (Power BI, Tableau, Looker) and semantic layer design
- Ability to analyze complex problems, define architectural options, and drive decisions
- Ability to work and collaborate with stakeholders across technical and business functions
- Excellent communication skills to translate complex technical architectures into clear business implications
- High autonomy, attention to detail, and ability to manage multiple client engagements simultaneously
Benefits
Comp & perks- A competitive salary
- A great working environment
- A steep learning curve with interesting and diverse topics to work on
- A healthy work-life balance
- Health insurance benefits