Senior Data Engineer (Python, Pandas & Numpy)

Lumiq

IndiaremotePosted 12 days ago

Skill Required

Data-EngineerETL-DeveloperPython-DeveloperData-Pipeline-EngineerSenior-Data-EngineeringPython-Data-EngineerData EngineerPython DeveloperPythonPandasNumPyJMeterElasticsearchSQL ServerPostgreSQLKubernetesbuildingMongoDBTestNGDockerdesignDevOpsETLSQLandFulltime

Key highlights

Level Required: Strong experience building and maintaining large-scale distributed data systems
Notable requirement: Expert-level proficiency in Python, especially data analysis/manipulation libraries like pandas, NumPy, and Polars
Notable requirement: Advanced SQL development skills with proven experience in performance optimization

Role overview

We are seeking a highly skilled ETL Data Engineer to re-engineer our existing data pipelines to extract data from a new data source (PostgreSQL / CURA system) instead of the current Microsoft SQL Server (CRM persistence store), while preserving the existing load patterns to Elasticsearch and MongoDB. The engineer will ensure this migration has zero impact on data quality, system performance, or end-user experience.

Responsibilities

Analyze existing ETL pipelines and their dependencies on Microsoft SQL Server as source systems.
Design and implement modifications to repoint ETL extractions from PostgreSQL (CURA) while preserving the current transformations and load logic into Elasticsearch and MongoDB.
Ensure end-to-end data integrity, quality, and freshness remain unaffected after the source switch.
Write efficient and optimized SQL queries to extract data from the new source.
Conduct performance testing to confirm no degradation of pipeline throughput or latency in production.
Work closely with DevOps and platform teams to containerize, orchestrate, and deploy the updated ETLs using Docker and Kubernetes.
Monitor post-deployment performance and handle any production issues proactively.
Document design, code, data mappings, and operational runbooks.

Requirements

Strong experience building and maintaining large-scale distributed data systems.
Expert-level proficiency in Python, especially data analysis/manipulation libraries like pandas, NumPy, and Polars.
Advanced SQL development skills with proven experience in performance optimization.
Working knowledge of Docker and Kubernetes.
Familiarity with Elasticsearch and MongoDB as data stores.
Experience working in production environments with mission-critical systems.