Sr Data Engineer
Role details
Job location
Tech stack
Job description
The Senior Data Engineer will design, build, and optimize data pipelines and transformation frameworks that support high-visibility acquisition reporting and marketing analytics use cases. You will work across AWS, Databricks, Unity Catalog, Snowflake and Airflow to create reliable, scalable solutions for ingesting and modeling marketing platform data.
You will collaborate closely with product, analytics, marketing operations, and stakeholder teams to ensure data accuracy, reliability, SLAs, and transparency for downstream dashboards., * Architect, build, and maintain scalable ETL/ELT pipelines for acquisition reporting using Databricks, PySpark, SQL, and Unity Catalog.
- Lead the modernization effort to migrate existing Snowflake-based SQL scripts and transformations into Databricks UC with best practices in governance and structured access.
- Design robust ingestion frameworks for marketing vendor data.
- Implement data quality checks, monitoring, and automated remediation using Databricks, Snowflake, Airflow, and internal frameworks.
- Develop metadata-driven, parameterized pipeline components to accelerate onboarding of new vendors and datasets.
- Partner with the Data Reliability Engineering team to integrate SLA-based incident detection, logging, alerting, and auto-recovery workflows.
- Collaborate with analytics and marketing stakeholders to understand reporting needs and ensure reliable dashboard data.
- Improve pipeline performance, reliability, logging, and observability.
- Contribute to engineering best practices, code reviews, technical design docs, and framework enhancements.
- Mentor junior engineers and contribute to team-wide architectural decisions.
Requirements
- 5+ years of experience as a Data Engineer or similar role.
- Strong proficiency in SQL (analytical SQL, complex joins, window functions).
- Hands-on experience with PySpark and/or Spark SQL in production.
- Strong understanding of data modeling, ETL/ELT design patterns, and distributed data processing.
- Experience building pipelines in Databricks, including Delta Lake, Unity Catalog, data governance, and Lakehouse patterns.
- Strong experience in AWS (S3, IAM, EC2, Glue, Lambda, or related services).
- Proficiency with Airflow or similar orchestration tools.
- Experience building robust ingestion pipelines and working with semi-structured formats (JSON, Parquet, CSV).
- Experience with Git/GitHub, CI/CD, and modern DevOps practices.
- Excellent communication skills and ability to work with cross-functional partners.
- Bachelor's degree in computer science, Information Systems or related field, * Master's degree in computer science, Information Systems or related field a plus
- Experience with marketing or customer acquisition data (Meta, Google Ads, Google CM360, TikTok, Twitter, Snapchat, Branch, AppsFlyer, Salesforce, etc.).
- Familiarity with data observability, SLA monitoring, incident workflows, or reliability engineering concepts.
- Exposure to data quality frameworks (Great Expectations, Deequ, Monte Carlo, or custom frameworks).
Benefits & conditions
The hiring range for this position in Santa Monica, California is $141,900 to $190,300 per year, in New York, New York is $148,700 to $199,400 per year, and in Seattle, Washington is $148,700 to $199,400 per year. The base pay actually offered will take into account internal equity and also may vary depending on the candidate's geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.