About this role
We are looking for a Data Engineer to join one of the Data Platform teams that works with the Marketing, Growth, partner, and financial data domains. You will be working with cutting edge cloud technologies (GCP, AWS, BigQuery, Databricks, K8s) and building a large scale data infrastructure for analytics, machine learning, and streaming/CDC data delivery. Build and operate batch and streaming ingestion into a layered BigQuery DWH (raw → ODS → data marts) using Airflow, Debezium CDC over Kafka with protobuf, Pub/Sub, and Dataflow Integrate external data sources end-to-end — marketing platforms (GA4, AppsFlyer, TikTok/Meta/Google Ads), payment providers, S3 buckets, and third-party APIs — including schema contracts, backfills, and reconciliation Engineer the data platform itself in Python: custom Airflow operators and connectors in a shared ETL framework, Kafka Connect on Strimzi (K8s), Cloud Functions, and API integrations with external providers Build CI/CD and change-management tooling for BigQuery: GitHub-based test-and-approval flows, SQL migration engines (Liquibase/Flyway/Bytebase), sandbox validation, backup and rollback Own reliability and correctness of pipelines: idempotency, deduplication, late-data handling, backfill and replay, freshness monitoring and alerting; write integration and unit tests Drive data governance and compliance: ITGC-compliant change management for BigQuery, IAM and least-privilege access, PII policy tags and DLP, Unity Catalog on Databricks, column-level lineage (OpenMetadata/Dataplex), and disaster-recovery planning Build internal data tools and platform services for agentic workflows with data — Streamlit apps, Slack bots, LLM-based agents and MCP servers that help teams find and use data Support analysts and business teams with data requests, fostering data-driven decision-making across the company Contribute to system design and architecture with the development team