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: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.    We appreciate you taking the time and look forward to reviewing your application  WellnessLiving is proud to be an equal opportunity employer. We base employment decisions solely on qualifications, experience, and business needs. We do not tolerate discrimination or harassment of any kind. All qualified applicants will receive consideration without regard to race, color, religion, creed, gender, gender identity or expression, sexual orientation, national origin, disability, age, genetic information, veteran status, marital or family status, or any other status protected by applicable laws.  We utilize AI to generate summaries of interview notes as part of our candidate evaluation process. This helps ensure a fair and consistent review while maintaining a human-centered hiring approach.