Senior Data Engineer

• Design and implement a comprehensive data lake architecture using modern cloud-native technologies
• Build scalable ETL/ELT pipelines for real-time and batch data processing across all data sources
• Establish data ingestion frameworks to collect data from application APIs, third-party services, and databases
• Architect automated data quality monitoring, validation, and alerting systems
• Create robust data warehousing solutions optimized for analytics and business intelligence
• Implement DataOps practices with automated testing and deployment pipelines (CI/CD for data)

• Develop and maintain Python-based data processing frameworks and utilities
• Build an automated data pipeline orchestration using Apache Airflow or similar tools
• Create streaming data processing solutions using Apache Kafka, Kinesis, or Pub/Sub
• Implement infrastructure as code for all data platform components (Terraform, CloudFormation)
• Establish feature stores and data models that support both operational and analytical workloads
• Optimize data storage costs and query performance across the entire platform
• Collaborate with product and business teams to identify key metrics, KPIs, and analytical requirements
• Build automated reporting dashboards and self-service business intelligence tools
• Support predictive modeling initiatives and A/B testing frameworks

• Minimum 5+ years of hands-on data engineering experience with increasing responsibility
• Preferred 7+ years in data engineering, analytics engineering, or data platform roles
• Proven track record of building data systems from scratch or leading data infrastructure transformations
• Experience working as a solo data engineer or in small, autonomous data teams

• Python proficiency required - demonstrated experience building data pipelines, ETL frameworks, and automation scripts
• SQL expertise - advanced knowledge of complex queries, performance optimization, and data modeling
• Strong experience with cloud platforms (AWS, GCP, or Azure) and their data services
• Proficiency with data lake technologies (Delta Lake, Apache Iceberg, or Apache Hudi)
• Experience with data orchestration tools (Apache Airflow, Prefect, Dagster, or similar)
• Knowledge of streaming data technologies (Apache Kafka, Kinesis, Pub/Sub)
• Familiarity with data warehouse technologies (Snowflake, BigQuery, Redshift, Databricks)
• Understanding of containerization (Docker, Kubernetes) and infrastructure as code
• Experience with version control systems (Git) and collaborative development workflows

• Background in real-time analytics and event-driven architectures
• Previous experience in startup or fast-paced environments
• Understanding of data privacy regulations (GDPR, CCPA) and security best practices
• Experience with performance monitoring and observability tools
• Knowledge of dimensional modeling and data warehouse design patterns

• Compensation includes a highly competitive salary, generous equity, medical, dental, and vision coverage paid 100% by the company, 401K benefits, and other travel-related perks
• $500 Annual Experience Stipend (Can be used at any of our client partners)

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.