Sr. Data Engineer – AWS Lambda & PostgreSQL
Location: Hempstead, NY or Basking Ridge, NJ or Columbus, OH or Dallas, TX Duration: 12 months MoI: Video Candidates local to these areas or someone who can relocate. Hybrid working model.
Role Overview: We are seeking a skilled Sr Data Engineer to design and implement scalable data pipelines using AWS Lambda that push structured and semi-structured data into a PostgreSQL data store. The role also requires experience in data modeling, Looker dashboard development, and strong SQL/database expertise to support reporting and analytics needs across the organization.
Key Responsibilities:
- Design, develop, and deploy serverless data ingestion pipelines using AWS Lambda
- Write and optimize Lambda functions to clean, transform, and push data into PostgreSQL
- Develop and maintain scalable, efficient data models supporting analytical workloads
- Create LookML models and build dashboards in Looker to enable self-service analytics
- Maintain database integrity, indexing, and performance optimization
- Collaborate with product, engineering, and analytics teams to understand data needs
- Build robust error-handling, logging, and retry mechanisms for data pipelines
- Ensure data governance, quality, and security best practices are followed
Required Skills:
- Overall 10+ years of experience
- 3–5 years of experience in Data Engineering or Backend Engineering roles
- Strong experience with AWS Lambda and serverless data architecture
- Proficient in Python or Node.js for writing Lambda functions
- Solid experience with PostgreSQL – schema design, optimization, and advanced SQL
- Proven expertise in data modeling for analytics and reporting
- Hands-on experience with Looker (LookML, dashboards, data exploration)
- Familiarity with AWS services like S3, CloudWatch, API Gateway, and IAM
- Excellent debugging, problem-solving, and communication skills
Good to Have:
- Experience integrating third-party APIs or webhooks into Lambda functions
- Familiarity with data warehousing concepts (e.g., Snowflake, Redshift, or BigQuery)
- Exposure to CI/CD for data pipelines using tools like GitLab or Jenkins
- Understanding of modern data stack tools (Fivetran, dbt, Airflow, etc.)
