Design, build, and optimize algorithm in Vertex ai.
Develop scalable data models, Algorithm supporting user 360 views, churn prediction, and recommendation engine inputs.
Lead integration across data sources: MySQL, BigQuery, Redis, Kafka, GCP Storage, and API Gateway.
Implement CI/CD for data pipelines using Git, dbt, and automated testing.
Define data quality checks and auditing pipelines for ingestion and transformation layers.
Mentor and guide junior AI/ML engineers on data modeling, algorithm performance tuning.
Partner with Data Science, ML, and Backend teams to productionize machine learning features in Snowflake.
Work closely with Legal, Security, and Infrastructure teams to ensure compliance, privacy, and governance of user data (PII).
Collaborate with the Director of Data Platforms and product stakeholders to translate business requirements into technical specifications.
Tune algorithm performance.
Establish data partitioning, clustering, and materialized views for fast query execution.
Build dashboards and monitors for pipeline health, job success, and data latency metrics (e.g., via Looker, Tableau, or Snowsight).
Establish and enforce naming conventions, data lineage, and metadata standards across schemas.
Lead code reviews, enforce documentation standards, and manage schema versioning.
Contribute to the company’s evolving data mesh and streaming architecture vision.

Requirements

5+ years of experience in AI/ML engineering, with **3+ years in Vertex.ai**.
Strong SQL and Python skills, with proven experience building **ETL/ELT** at scale.
Deep understanding of algorithm** performance tuning**, **query optimization**, and **warehouse orchestration**.
Experience with **data pipeline orchestration** (Airflow, Prefect, dbt, or similar).
Solid understanding of **data modeling** (Kimball, Data Vault, or hybrid).
Proficiency in **Kafka**, **GCP**, or **AWS** for real-time or batch ingestion.
Familiarity with **API-based data integration** and **microservice architectures**.
**Preferred**
Experience lead **machine learning teams** or/and deploying **ML feature pipelines**.
Background in **ad-tech, gaming, or e-commerce** recommendation systems.
Familiarity with **data contracts** and **feature stores** (Feast, Tecton, or custom-built).
Experience managing small data engineering teams and setting technical direction
Strong ownership and ability to work autonomously in a fast-paced environment.
Excellent cross-functional communication — can translate between engineering and business.
Hands-on problem solver who balances velocity with reliability.
Collaborative mentor who raises the bar for team quality and discipline

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

algorithm designdata modelingperformance tuningETLELTdata pipeline orchestrationquery optimizationdata quality checksautomated testingmachine learning

Soft Skills

mentoringcross-functional communicationproblem solvingownershipcollaborationleadershipautonomyguidanceteam qualitydiscipline