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