Senior Data Engineer

Established in 2012, Factspan is a leading pure‑play analytics company with expertise in converting data into actionable business insights. We provide the expert services that organizations need to establish their own analytics centre of excellence. Our dedicated team of data scientists and consultants partner with the clients to provide analytics solutions that organizations need every day to make data‑driven decisions. Headquartered in Seattle, we also have offices in Bangalore, India. Our clients include Fortune 100/500 companies in Retail, Financial Services, Insurance, Hospitality, and Technology industries. Our Vision is to be the foremost player in the business analytics space. Location: Atlanta, GA Work Model: Hybrid – 2 days onsite Experience: 8–12+ years (Retail) Employment Type: Full‑time Role Overview We are looking for a strong MarTech Data Engineer to support a large retail client’s Marketing Technology and Customer Data ecosystem. The role will focus on building scalable data pipelines, integrating marketing and customer data sources, enabling audience activation, supporting analytics use cases, and creating reliable data services using GCP, Python, SQL, MongoDB, and APIs. The ideal candidate should have hands‑on experience in cloud data engineering, API‑driven integrations, data modeling, pipeline orchestration, and working with marketing, customer, campaign, loyalty, and digital engagement datasets. Key Responsibilities Design, build, and maintain scalable data pipelines on Google Cloud Platform using services such as BigQuery, Cloud Storage, Cloud Functions, Cloud Run, Pub/Sub, Dataflow, Dataproc, Composer, and related GCP services. Develop robust Python‑based data processing frameworks for ingestion, transformation, validation, enrichment, and publishing of MarTech data. Write optimized SQL queries, stored procedures, and data models to support analytics, customer segmentation, campaign reporting, and downstream activation. Build and manage integrations with internal and external systems using REST APIs, GraphQL APIs, webhooks, batch feeds, and event‑based data exchange patterns. Work with MongoDB for semi‑structured data storage, customer profile data, metadata services, API response persistence, and operational data use cases. Partner with MarTech, analytics, data science, product, and platform teams to understand business requirements and translate them into scalable data solutions. Support data quality checks, reconciliation, monitoring, logging, alerting, and production issue resolution. Ensure secure handling of customer, marketing, loyalty, and behavioral data with proper access controls, encryption, masking, and compliance‑aware engineering practices. Required Skills Strong hands‑on experience with GCP data engineering and cloud‑native architecture. Advanced Python programming skills for ETL, APIs, automation, orchestration, and data validation. Strong SQL expertise, especially with BigQuery or similar cloud data warehouses. Experience with MongoDB, document modeling, indexing, aggregation pipelines, and performance tuning. Solid experience building and consuming APIs, handling authentication, pagination, retries, error handling, and data contracts. Preferred Skills Experience in retail, e‑commerce, loyalty, customer identity, marketing analytics, campaign platforms, or CDP ecosystems. Knowledge of CI/CD, Git, Airflow/Composer, Docker, Terraform, and production deployment practices. Ideal Candidate A hands‑on engineer who can operate independently, communicate clearly with business and technology teams, and build reliable MarTech data products that support analytics, personalization, campaign execution, and customer engagement. #J-18808-Ljbffr Factspan