Design, build, and maintain scalable ETL/ELT pipelines that ingest, transform, and deliver data across the organization.
Develop and optimize distributed data processing jobs using Python for large-scale data transformation and aggregation.
Architect and manage PostgreSQL schemas, tables, indexes, and query performance to support downstream analytics and reporting.
Build and maintain Python-based data workflows to orchestrate, validate, and deliver data reliably across environments.
Monitor and improve data quality, freshness, and completeness through automated checks, alerting, and observability tooling.
Design and manage cloud-based data infrastructure on AWS
Partner with data analysts and stakeholders to translate requirements into well-modeled, maintainable data products.
Maintain documentation for pipelines, data models, data lineage, and infrastructure.
Troubleshoot pipeline failures and data issues, providing timely root-cause analysis and remediation.

Experience: 3+ years of professional experience in data engineering.
PostgreSQL: Schema design, indexing strategies, query optimization, and performance tuning.
Python: Pipeline development, data validation, and orchestration frameworks.
Distributed Processing and Storage: Hands-on production experience with tools such as AWS Athena, Apache Spark, etc.
ETL/ELT: Proven experience designing and implementing pipelines in production.
Cloud: AWS (S3, EKS, Glue, Athena).
Data Modeling: Dimensional modeling, data warehousing patterns, and reproducible transformations.
Engineering Practices: Git workflows, code reviews, testing, and CI/CD.
LLM AI Agents: ability to use agents effectively to increase output.

Data Engineer – 4-month contract

Key skills