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 :
Experience : 5+ years in data engineering with a proven record in modernizing legacy data systems and driving large-scale migration initiatives.Cloud ETL Expertise : Proficient in AWS Glue, Apache Spark / PySpark, and modular transformation frameworks like dbt .Data Modeling : Strong grasp of domain-driven design , bounded contexts, and BI-friendly modeling approaches (star / snowflake / data vault).Data Migration : Experience with full lifecycle migrations including schema / data mapping, reconciliation, and exception handling.Databases : SQL : MySQL, Aurora, Redshift & NoSQL : MongoDB, DocumentDBProgramming : Strong Python skills for data wrangling, pipeline automation, and API interactions.Data Architecture : Hands-on with data lakes, warehousing strategies, and hybrid cloud data ecosystems.Compliance & Security : Track record implementing governance, data cataloging, encryption, retention, lineage, and RBAC.DevOps Practices : Git, CI / CD pipelines, Docker, and test automation for data pipelines.Preferred Qualifications :
Experience with streaming data platforms like Kafka, Kinesis, or CDC tools such as DebeziumFamiliarity with orchestration platforms like Airflow or PrefectBackground in analytics, data modeling for AI / ML pipelines , or ML-ready data preparationUnderstanding of cloud-native data services (AWS Glue, Redshift, Snowflake, BigQuery, etc.)Degree in Computer Science, Engineering, or equivalent fieldStrong written and verbal communication skillsSelf-starter with ability to navigate ambiguity and legacy system complexityExposure to generative AI, LLM fine-tuning , or feature store design is a plusPlease note that only those selected for an interview will be contacted.
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