Senior Data Engineer

The Company
Well-established consumer software company with a large global footprint. Strong benefits: bonus, pension, medical/dental/vision, generous PTO, and paid parental leave.
The Role
Senior IC role on a data innovation team responsible for designing, building, and operating the data architecture that powers analytics, ML/AI initiatives, and business intelligence across a large-scale consumer software platform. You'll work at the intersection of data engineering, data science, and data quality — partnering closely with stakeholders, data scientists, and product teams.
Responsibilities
Design and deploy comprehensive data architecture capturing structured and unstructured data from diverse internal and external sources
Build resilient ETL/ELT pipelines routing data across cloud structures, local databases, and other storage forms
Implement data quality frameworks — validation, monitoring, and automated recovery strategies
Collaborate with data scientists to enable advanced analytics, predictive modeling, and ML initiatives
Develop web-enabled, self-service analytics solutions that democratize data access company-wide
Apply AI/ML and big-data techniques to automate data cleansing, transformation, and enrichment
Leverage MCP (Model Context Protocol) to connect enterprise applications and automate data flows
Ensure secure, scalable, and compliant data ingestion with appropriate PII handling
Troubleshoot pipeline issues, optimize performance, and participate in on-call rotations
Mentor junior team members and contribute to data engineering practice growth
Requirements
8+ years of hands-on ETL/ELT pipeline development across varied data sources
Strong programming skills in Python, Scala, or Java (production-quality code)
Experience with modern data platforms — Snowflake, Databricks, Apache Spark, Kafka, Airflow
Cloud platform experience — AWS, Azure, or GCP and their native data services
Experience with real-time data processing and streaming architectures
Solid data modeling, warehousing, and dimensional modeling fundamentals
Knowledge of containerization and orchestration (Docker, Kubernetes)
Practical knowledge of MCP and AI-assisted development tools
Familiarity with DataOps and MLOps practices
Experience managing sensitive/PII data with attention to compliance and governance
Strong communication skills across technical and non-technical stakeholders
Preferred
Background in data science or analytics
Experience in client-facing or Professional Services roles