Design and build scalable, reliable data pipelines

for high-volume, high-velocity data

Own data ingestion from multiple sources (product events, CRM, third-party tools, internal systems)

Build and maintain CDC Pipelines

, ETL/ELT workflows

, data models, and transformations

Enable real-time and near-real-time analytics

using Elastic Search to power AI workflows and sales playbooks

Partner with ML engineers to support training, feature engineering, and inference pipelines

Ensure data quality, observability, and reliability

across systems

Optimize pipelines for performance, cost, and scalability

Define best practices for schema design, versioning, and documentation

Mentor junior engineers and raise the overall data engineering bar

5+ years of experience

as a Data Engineer or in a similar backend/data-heavy role

Strong hands-on experience with:

Data pipelines and orchestration (Airflow, Mage AI, Prefect, or similar)

Data warehouses / search engines (Elasticsearch, BigQuery, Snowflake, Redshift, Databricks, etc.)

SQL at scale and data modeling best practices

Solid programming experience in Java (Sprint Boot)

and Python

(or equivalent)

Experience working with event-driven or streaming data

(RabbitMQ, Kafka, Pub/Sub, Kinesis, etc.) is a strong plus

Familiarity with cloud infrastructure

(AWS (preferred)/ GCP / Azure)

Strong understanding of data reliability, monitoring, and debugging

Ability to work independently, take ownership, and ship end-to-end systems

Clear communication and the ability to collaborate across product, ML, and engineering teams

Experience in high-growth startups

or 0→1 / 1→n scaling phases

Strong opinions on data architecture—and the ability to defend them pragmatically