Sr. Data Engineer - AWS Lambda & PostgreSQL

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.)