About OmniGTM.ai

AEPG is building an AI-native Go-To-Market (GTM) platform designed to eliminate operational silos across marketing, sales, and customer success. OmniGTM.ai unifies CRM data, third-party enrichment, intent signals, user interactions, and activation workflows into a single, governed source of truth that enterprise teams can trust.

As a foundational engineering hire, you will directly influence the platform’s data architecture, pipeline automation standards, and database change management practices that enable reliable AI-driven workflows at scale.

Job Summary

We are hiring a Senior Data Engineer to design, implement, and operate OmniGTM.ai’s end-to-end data foundation—including automated pipeline orchestration and database change automation using Liquibase. You will own production-grade ingestion, transformation, CDC/streaming patterns, schema evolution, and observability across a polyglot stack (MongoDB Atlas + Amazon Aurora PostgreSQL + AWS services).

This role expects you to treat data pipelines and database migrations as first-class software: version-controlled, test-gated, repeatable across Dev/Stage/Prod, and resilient under real-world change.

Key Responsibilities

1) Automated Data Pipelines (Batch + Near-Real-Time)

Design and operate high-throughput ETL/ELT pipelines processing CRM objects, enrichment provider feeds, intent signals, product usage events, and activation outcomes.

Build distributed processing workflows using PySpark/Spark, EMR (or equivalent), and lakehouse patterns (Delta/Iceberg/Hudi as appropriate).

Implement CDC and incremental ingestion patterns (e.g., log-based capture, timestamp-based deltas, idempotent replay, late-arriving data handling).

Use orchestration tooling such as Airflow/MWAA, AWS Step Functions, AWS Glue, or an equivalent orchestrator to ensure reliability and traceability.

2) Database Change Automation (Liquibase as a Standard)

Establish and enforce schema-as-code practices for Aurora PostgreSQL using Liquibase: versioned changelogs, rollback strategy, and environment promotion.

Build CI/CD gates for database changes: lint/validate, dry-run, diff checks, migration tests, and automated deployment to Dev/Stage/Prod.

Define safe patterns for schema evolution: backward compatible changes, feature-flagged rollout, dual-write/dual-read when needed, and zero/near-zero downtime migrations.

Maintain a clear boundary between schema migrations (Liquibase) vs data backfills and bulk migrations (pipeline jobs), while supporting controlled reference/seed data where appropriate.

3) Unified Data Foundation & Data Contracts

Own schema design and data modeling across MongoDB Atlas and Aurora PostgreSQL, aligned to a unified GTM data model.

Implement data contracts and validation: required fields, allowed values, nullability policies, and compatibility rules for producers/consumers.

Define a pragmatic strategy for “source of truth” across systems (CRM vs enrichment vs inferred/AI-enriched fields), including conflict resolution and lineage.

4) AI/ML Enablement (Data for Scoring, Personalization, and Analytics)

Build datasets and feature pipelines to support predictive scoring, segmentation, propensity models, and personalization loops.

Provide AI teams with high-quality, explainable, low-latency data for training, offline evaluation, and production inference.

Create feedback loops from activation outcomes (opens/clicks/replies/conversions) back into scoring and recommendation systems.

5) Governance, Observability, and Cost/Performance Optimization

Implement monitoring and alerting for pipeline health, freshness, correctness, and SLA adherence (CloudWatch/X-Ray and/or equivalents).

Define data quality checks (duplicates, drift, anomalies, referential integrity), and operational runbooks for incidents.

Continuously optimize compute and storage: Spark tuning, partitioning strategy, indexing, query plans, and cost controls across AWS + databases.

6) Collaboration and Technical Leadership

Partner with Solution Architecture, AI Engineering, and Product leadership to align data systems with business workflows and GTM lifecycle requirements.

Mentor engineers on modern data engineering practices: reproducibility, idempotency, schema evolution, CI/CD for data, and operational excellence.

Influence platform strategy and architectural decisions with clear tradeoffs and written decision records.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field (or equivalent experience).
• 7+ years of experience building and operating production-scale data pipelines.
• Strong Python skills for pipeline development, automation, and services.
• Strong SQL skills and hands-on experience with relational systems (preferably Aurora PostgreSQL).
• Demonstrated experience with schema evolution and database change management in production—Liquibase (preferred) or equivalent tooling.
• Experience with orchestration tools such as Airflow/MWAA, AWS Glue, Step Functions, or equivalent.
• Experience with distributed processing: Spark/PySpark, EMR (or comparable).
• Experience with NoSQL systems such as MongoDB Atlas (or DynamoDB).
• Practical experience in AWS: S3, IAM, Lambda, Kinesis (or equivalent streaming), Glue, and security best practices.

Preferred Qualifications

• Experience with CDC tooling and patterns (e.g., AWS DMS, Debezium, Kafka-based CDC).
• Familiarity with Kafka and event-driven architectures (DLQs, retries, replay, ordering, idempotency).
• Experience supporting ML data workflows (feature generation, training sets, evaluation loops, MLOps integrations).
• Familiarity with dbt or similar transformation frameworks.
• Exposure to Docker/Kubernetes and IaC tooling (Terraform, AWS CDK).
• Understanding of SOC2 / ISO27001 / GDPR-style controls as they apply to data systems.

Why Join OmniGTM.ai

Own the data foundation + automation standards for an AI-native GTM platform built for enterprise scale.

Build “real” production systems: schema governance, CDC, automated pipeline releases, and feedback loops that improve AI outcomes over time.

Influence core architectural decisions across polyglot persistence, data contracts, pipeline reliability, and cost/performance.

Collaborate closely with senior AI and platform leaders building a next-generation GTM operating system.

How to Apply

Send your resume and a brief cover letter outlining relevant experience to [email protected] with the subject line Senior Data Engineer – OmniGTM.ai.

OmniGTM.ai is an equal opportunity employer. We value diversity and encourage applications from individuals of all backgrounds.