Senior Data Engineer

October 28

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WellnessLiving

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Wellness • SaaS • Fitness

WellnessLiving is a comprehensive software solution designed for the fitness and wellness industry. It offers a suite of tools for managing businesses such as yoga studios, fitness centers, dance studios, and wellness businesses. The platform includes features for online booking, staff and client management, reporting, payments, and marketing solutions like email and SMS marketing. It also integrates with various services like QuickBooks and Google, providing users with a cloud-based enterprise solution that enhances operational efficiency and client engagement. WellnessLiving aims to boost business growth, retention, and streamline daily operations.

201 - 500 employees

🧘 Wellness

☁️ SaaS

📋 Description

• Lead the decomposition of monolithic database structures into domain-aligned schemas that enable service independence and ownership. • 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. • Define logical and physical domain-driven data models (star/snowflake schemas, data marts) to serve cross-functional needs, BI, operations, streaming, and ML. • Design strategies for extracting, validating, and restructuring data from legacy systems with embedded logic and incomplete normalization. • Administer, optimize, and scale SQL (MySQL, Aurora, Redshift) and NoSQL (MongoDB) platforms to meet high-availability and low-latency needs. • Leverage AWS Glue Catalog, Crawlers, Lambda, and S3 to manage and orchestrate modern, cost-efficient data pipelines. • Enforce best practices around cataloging, lineage, retention, access control, and security, ensuring compliance with GDPR, CCPA, PIPEDA, and internal standards. • Implement observability (CloudWatch, logs, metrics) and performance tuning across Spark, Glue, and Redshift workloads. • Work with architects, analysts, product managers, and data scientists to define, validate, and prioritize requirements. • Maintain technical documentation (data dictionaries, migration guides, schema specs) and mentor junior engineers in engineering standards.

🎯 Requirements

• 5+ years in data engineering with a proven record in modernizing legacy data systems and driving large-scale migration initiatives. • Proficient in AWS Glue, Apache Spark/PySpark, and modular transformation frameworks like dbt. • Strong grasp of domain-driven design, bounded contexts, and BI-friendly modeling approaches (star/snowflake/data vault). • Experience with full lifecycle migrations including schema/data mapping, reconciliation, and exception handling. • SQL: MySQL, Aurora, Redshift & NoSQL: MongoDB, DocumentDB. • Strong Python skills for data wrangling, pipeline automation, and API interactions. • Hands-on with data lakes, warehousing strategies, and hybrid cloud data ecosystems. • Track record implementing governance, data cataloging, encryption, retention, lineage, and RBAC. • Git, CI/CD pipelines, Docker, and test automation for data pipelines.

🏖️ Benefits

• Based on experience • Benefits • Vacation