DATA ARCHITECT

3 days ago

Pasig, Philippines Seven Seven Global Services, Inc. Full time

1. Experience Typically requires 8-10 years of experience in data, with at least 3-5 years in architecture roles or designing complex data systems. 2. Data Modeling & Design Data Warehousing: Mastery of Star Schema and Snowflake Schema Dimensional modeling (Kimball methodology) Data Vault 2.0 for enterprise environments Slowly Changing Dimensions (SCD Types 1-6) Database Design: Strategic normalization and denormalization Partitioning and sharding Advanced indexing and query optimization 3. Tools & Technologies Cloud Platforms (essential): AWS (Redshift, S3, Glue, Lake Formation, Athena) Azure (Synapse, Data Factory, Databricks, ADLS) Data Warehouses/Lakes: Snowflake, Redshift, BigQuery Databricks, Azure Synapse Data Lake architectures (Bronze/Silver/Gold layers) ETL/ELT Tools: dbt (highly valued currently) Apache Airflow Informatica, Talend, SSIS Fivetran, Stitch Streaming: Kafka, Kinesis, Event Hubs Spark Streaming, Flink Databases: SQL: PostgreSQL, MySQL, SQL Server, Oracle NoSQL: MongoDB, Cassandra, DynamoDB, CosmosDB 4. System Design & Architecture Designing scalable data pipelines (batch and real-time) Lambda and Kappa architectures Microservices data patterns Event-driven architectures Data mesh principles (increasingly relevant) Master Data Management (MDM) Metadata management Data lineage and cataloging 5. Data Governance & Security Governance: Data quality frameworks Data cataloging (Collibra, Alation, Purview) Compliance (GDPR, LGPD, HIPAA, SOC2) Data stewardship models Security: Encryption (at rest and in transit) Row-level and column-level security Identity and Access Management (IAM) Data masking and tokenization Network security (VPCs, private endpoints) Audit logging 6. Performance & Optimization Query optimization and tuning Cost optimization (cloud spending) Capacity planning Monitoring and alerting (DataDog, CloudWatch, Grafana) Performance benchmarking 7. Leadership & Customer-Facing Skills Technical Leadership: Code reviews and best practices Establishing standards and frameworks Technical decision-making Stakeholder Management: Communication with C-level and business stakeholders Translating business requirements into technical solutions Clear technical presentations and documentation Managing expectations and roadmaps Facilitating design workshops Soft Skills: Influencing without direct authority Negotiating technical trade-offs Strategic long-term thinking 8. Complementary Knowledge Programming: Advanced SQL (window functions, CTEs, query optimization) Python or Scala Scripting (Bash, PowerShell) DevOps & Infrastructure as Code: Terraform, CloudFormation, ARM templates CI/CD for data pipelines Git and version control Docker, Kubernetes (basic) Business Intelligence: Understanding BI tools (Tableau, Power BI, Looker) Semantic layers and data marts Business KPIs and metrics Machine Learning (desirable): Feature stores MLOps pipelines Model serving infrastructure 9. Current Valued Trends Modern Data Stack (dbt, Snowflake) Data Mesh and decentralization Real-time analytics Cloud-native architectures DataOps practices Open table formats (Delta Lake, Iceberg, Hudi) Summary: A Senior Data Architect needs to be a technical generalist with depth in specific areas, capable of balancing technical requirements with business needs, and with strong communication skills to work with stakeholders at all organizational levels. #J-18808-Ljbffr

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