Senior Data Engineer

Responsibilities

• Monolith-to-Microservices Data Transition : Lead the decomposition of monolithic database structures into domain-aligned schemas that enable service independence and ownership.
• Pipeline Development & Migration : Build and optimize ETL / ELT workflows using Python, PySpark / Spark, AWS Glue, and dbt , including schema / data mapping and transformation from on-prem and cloud legacy systems into data lake and warehouse environments.
• Domain Data Modeling : Define logical and physical domain-driven data models (star / snowflake schemas, data marts) to serve cross-functional needs, BI, operations, streaming, and ML.
• Legacy Systems Integration : Design strategies for extracting, validating, and restructuring data from legacy systems with embedded logic and incomplete normalization.
• Database Management : Administer, optimize, and scale SQL (MySQL, Aurora, Redshift) and NoSQL (MongoDB) platforms to meet high-availability and low-latency needs.
• Cloud & Serverless ETL : Leverage AWS Glue Catalog, Crawlers, Lambda, and S3 to manage and orchestrate modern, cost-efficient data pipelines.
• Data Governance & Compliance : Enforce best practices around cataloging, lineage, retention, access control, and security, ensuring compliance with GDPR, CCPA, PIPEDA , and internal standards.
• Monitoring & Optimization : Implement observability (CloudWatch, logs, metrics) and performance tuning across Spark, Glue, and Redshift workloads.
• Stakeholder Collaboration : Work with architects, analysts, product managers, and data scientists to define, validate, and prioritize requirements.
• Documentation & Mentorship : Maintain technical documentation (data dictionaries, migration guides, schema specs) and mentor junior engineers in engineering standards.

Required Qualifications :

Preferred Qualifications :

Please note that only those selected for an interview will be contacted.

We appreciate you taking the time and look forward to reviewing your application!

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