Data Lakehouse Architect

Experience

Role Overview

Lead Data Engineer

Key Responsibilities

• Build and operate end-to-end ingestion pipelines from 20+ heterogeneous source systems including Oracle Retail, WMS, TMS, Loyalty platforms, and third-party APIs into the Bronze layer
• Implement CDC pipelines for real-time and near-real-time data capture across relational and NoSQL sources
• Design and build Silver and Gold transformation layers including data cleansing, enrichment, SCD Type 1 and 2, and complex business rule application
• Develop and maintain orchestration workflows with automated retry, failure alerting, and SLA tracking
• Implement data quality checks, validation rules, and reprocessing/backfill capabilities
• Enforce security policies, PII masking, row-level and column-level access control as defined by the Architect
• Enable consumption layers for Tableau, direct SQL users, and downstream API integrations
• Support historical data migration from legacy cloud data warehouse into the lakehouse
• Maintain pipeline documentation, source-to-target mappings, and data dictionaries
• Build clean, well-structured data pipelines that support AI/ML feature engineering and model training workflows

Required Skills And Experience

• 12+ years in data engineering with hands-on experience across ingestion, transformation, orchestration, and governance layers in production lakehouse environments
• Proficient in Python and PySpark for pipeline development and custom connector building
• Hands-on experience with cloud data services (e.g. S3/Blob Storage, Glue, DMS, Kinesis/Event Hubs, EMR or equivalent)
• Strong CDC implementation experience using DMS, Debezium, or equivalent across Oracle, RDS, and NoSQL sources
• Experienced with Delta Lake/Apache Iceberg ACID transactions, merge/upsert operations, and partition management at scale
• Strong hands-on experience with dbt for SQL-based transformation layers
• Proficient with orchestration tools such as Apache Airflow (MWAA), Dagster, or Step Functions
• Experienced with data quality frameworks such as Great Expectations, Deequ, or dbt tests
• Hands-on with security implementation: IAM policies, PII masking, column-level and row-level access control
• Strong SQL skills and dimensional modeling (star schema, snowflake schema) for BI consumption layers
• Retail or e-commerce domain experience (Oracle Retail, Magento, Shopify, WMS, TMS) is a strong advantage
• Familiarity with AI/ML pipeline requirements including feature store design, data preparation for model training, and vector database integration

Preferred And Nice-To-Have Platforms

• AWS (Lake Formation, Glue, Redshift, EMR, SageMaker)
• Databricks
• Snowflake
• Microsoft Fabric
• Google BigQuery
• Dataiku

Education

• Bachelor’s or master’s degree in computer science, Information Systems, or a related field
• Cloud Data Analytics or Solutions Architect certification from a major cloud provider (AWS, Azure, or GCP) is a strong advantage
• Databricks Certified Data Engineer or Snowflake SnowPro certifications are a plus
• Interested candidates share their resume at uma.jangra@glovetalent.com !!