Senior Data Engineer - Online
About The Position
We are seeking a Senior Data Engineer to build and scale a modern data platform supporting product analytics, lifecycle marketing, ecommerce insights, and machine learning pipelines in a SaaS environment. The role requires solid hands-on experience with cloud data stacks and close collaboration with product, marketing, and ML teams. The work includes a mix of new pipeline development and the maintenance and evolution of long-lived pipelines, with a strong emphasis on correctness, stability, and thoughtful iteration. You’ll operate in areas where data issues can directly affect product workflows or customer experiences, making high data quality, proactive monitoring, and operational rigor essential. About the Team The Online Data team owns the data foundations that support online product experiences, lifecycle marketing, and revenue analytics. Our domain includes workflows that directly affect product operations and customer interactions, not just reporting. We work primarily in Snowflake ,Databricks , DBT, with Airflow-based orchestration, and place a solid emphasis on proper dimensional modeling, consistent business definitions, and clearly defined ownership boundaries. The team values engineer who take a long-term view of data systems and help raise the bar through thoughtful design, code reviews, and shared standards.
Requirements
- Possess experience with Snowflake, dbt , Apache Airflow, solid SQL, and data modeling, working with Apache Spark / PySpark and Databricks
- Handle event-driven, telemetry, and product analytics data, including ecommerce data models and SaaS business metrics (MRR, ARR, churn, retention)
- Apply workflow orchestration, monitoring, data quality best practices, and using data observability tools
- Build and maintain machine learning model pipelines and supports lifecycle marketing analytics
- Operate across cloud platforms (AWS, Azure, GCP) and implementing CI/CD for data pipelines
- Leverage streaming platforms such as Kafka and Kinesis
Responsibilities
- Designing, building, and maintaining scalable data pipelines using Airflow, Spark, and Databricks, including ML pipelines for feature engineering, training, and inference
- Developing ELT workflows and analytics-ready data models with dbt , architecting and optimizing Snowflake schemas for performance and cost
- Building and managing pipelines for product telemetry and event-based data (users, sessions, events), supporting ecommerce metrics such as orders, subscriptions, revenue, and LTV
- Enabling lifecycle marketing analytics (acquisition, activation, retention, churn) with reliable, high-quality data
- Ensuring data quality, reliability, monitoring, and cost optimization across the data platform
- Collaborating with Product, Marketing, Analytics, and ML teams, mentoring junior engineers, and contributing to data architecture decisions
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
Number of Employees
5,001-10,000 employees
