mniGTM.ai is building the first unified, AI-powered Go-To-Market (GTM) platform designed to eliminate silos across marketing, sales, and customer success. Our platform integrates advanced AI, scalable cloud-native data infrastructure, and explainable insights to help enterprise teams reach the right accounts, at the right time, with the right plays.
We serve global B2B organizations by connecting CRM data, third-party enrichment, and intent signals into a single source of truth. The result: accelerated revenue cycles, intelligent activation, and transparent insights that teams can trust.
Joining OmniGTM.ai means shaping the future of GTM digital technology with cutting-edge AI, enterprise-grade architecture, and a vision to transform how companies grow.
Job Summary
We are seeking an experienced Senior Data Engineer to design, build, and maintain the scalable data foundation that powers OmniGTM.ai. This role is central to ensuring our platform modules—Compass (growth strategy), Signals (leads data and scoring), Plays (outreach), Insights (analytics), and Assist (ai-powered coaching)—operate with high-quality, real-time, and explainable data.
You will architect and operate robust pipelines across MongoDB Atlas, Amazon Aurora PostgreSQL, and AWS-native services. Your work will directly enable AI/ML engineers, product teams, and GTM stakeholders to access clean, reliable data for advanced analytics, predictive scoring, and activation across the full GTM lifecycle.
Key Responsibilities Data Pipeline Architecture & Development
● Design and maintain high-throughput ETL/ELT pipelines ingesting from CRM (Salesforce, HubSpot, Dynamics), enrichment providers (ZoomInfo, Bombora, Crunchbase), and user interaction data.
● Ensure pipelines are resilient, fault-tolerant, and optimized for speed and cost.
Polyglot Data Infrastructure
● Manage both MongoDB Atlas (flexible schema, AI embeddings, vector search) and Amazon Aurora PostgreSQL (transactional and relational data).
● Operate data lakes (Amazon S3) for ingestion, raw storage, and archival.
Data Modeling & Governance
● Define schemas that support marketing, sales, and customer success workflows across the GTM lifecycle.
● Implement governance and lineage frameworks to ensure accuracy, security, and compliance.
AI/ML Enablement
● Build infrastructure to support feature engineering, training datasets, and inference-ready data feeds for AI-powered modules (Signals scoring, Compass recommendations, Assist insights).
● Partner with AI Engineers to optimize model pipelines for performance and explainability.
Observability & Optimization
● Instrument pipelines for monitoring (CloudWatch, X-Ray) and telemetry across ingestion, transformation, and consumption.
● Continuously tune queries, pipelines, and storage for cost efficiency and low-latency access.
Collaboration & Leadership
● Work closely with cross-functional teams—AI engineers, solution architects, product managers, and GTM strategists—to align data solutions with business outcomes.
● Mentor junior engineers in best practices for cloud-native and AI-driven data engineering.
Required Qualifications
● Bachelor’s or Master’s degree in Computer Science, Engineering, or related technical field.
● 7+ years of experience in Data Engineering with proven expertise in large-scale, production-grade pipelines.
● Strong programming skills in Python (data engineering frameworks, APIs, automation).
● Proficiency in SQL and relational databases (Aurora PostgreSQL preferred).
● Hands-on with ETL/ELT tools (Airflow, AWS Glue, or equivalent).
● Deep knowledge of AWS data stack: S3, Glue, Redshift, Lambda, Kinesis, IAM.
● Experience with NoSQL (MongoDB Atlas, DynamoDB) for unstructured and AI-enriched data.
● Understanding of data modeling, schema design, and governance frameworks.
● Strong problem-solving skills with heterogeneous data sources.
Preferred Qualifications
● Experience supporting AI/ML pipelines and MLOps practices (e.g., SageMaker, feature stores).
● Familiarity with streaming technologies (Kafka).
● Exposure to containerization/orchestration (Docker, Kubernetes).
● Experience with Infrastructure as Code (Terraform, AWS CDK).
● Knowledge of data security/privacy frameworks (GD
