Type: Long-term contract

We are building a core data platform for a high-growth e-commerce company. The team needs to move from fragmented scripts and dashboards to a unified, automated, and trusted data foundation to support personalization and real-time analytics.

Responsibilities:

• Design and build automated CI/CD pipelines for data transformations, ETL/ELT, and ML model training.

• Implement a robust framework for data quality testing, validation, and proactive monitoring.

• Develop and maintain infrastructure-as-code templates for data pipeline orchestration and environment management.

• Establish and automate metadata collection, data lineage tracking, and pipeline observability.

• Create standards and tools to enable self-service data pipeline deployment for analytics and data science teams.

Requirements:

• Experience in building, automating, and maintaining data pipelines (5+ years).

• Experience with Python and SQL for engineering tasks.

• Experience with orchestration tools (Airflow, Dagster, Prefect) and modern data stack components.

• Proven track record of implementing data quality checks and testing in a CI/CD context.

• Experience with infrastructure-as-code (Terraform, CloudFormation) and CI/CD platforms (GitLab CI, GitHub Actions).

Differentiator:

• Practical experience implementing a DataOps methodology or internal data platform.

• Knowledge of data discovery and lineage tools (DataHub, Amundsen).

Nice to have:

• Experience with Snowflake or BigQuery.

• Familiarity with Streamlit for building simple data apps.