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, DocumentDB
• Programming: 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 Debezium
• Familiarity with orchestration platforms like Airflow or Prefect
• Background in analytics, data modeling for AI/ML pipelines, or ML-ready data preparation
• Understanding of cloud-native data services (AWS Glue, Redshift, Snowflake, BigQuery, etc.)
• Degree in Computer Science, Engineering, or equivalent field
• Strong written and verbal communication skills
• Self-starter with ability to navigate ambiguity and legacy system complexity
• Exposure to generative AI, LLM fine-tuning, or feature store design is a plus

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.