Lead Data Engineer – Python

Job Description

Role Overview

We are looking for a Data Lead Engineer to drive the design, development, and scalability of our data platform. This role is hands-on and focuses on building robust data pipelines, leading engineering efforts, and ensuring high-performance data systems in production.

What You’ll Do

– Design and build scalable data pipelines for real-time and batch processing

– Lead development of cloud-native, distributed data systems

– Own end-to-end data engineering workflows including ingestion, transformation, and orchestration

– Ensure performance, reliability, and observability of data systems

– Collaborate with product and engineering teams to translate business needs into technical solutions

– Mentor and guide data engineers; drive best practices in coding and data quality

– Contribute to architecture decisions with a hands-on approach

What You Bring :

– 11+ years of experience in software/data engineering

– Strong hands-on experience with data processing frameworks : Spark, Kafka, Flink, Airflow or Dagster

– Expertise in Python (PySpark, Pandas, NumPy, Dask)

– Experience with distributed systems and data pipelines

– Knowledge of microservices, REST/gRPC APIs, and event-driven architecture

– Experience with databases & warehouses : PostgreSQL, MongoDB, Cassandra, Snowflake, BigQuery, or Redshift

– Familiarity with cloud platforms (AWS/Azure/GCP)

– Experience with Docker, Kubernetes, CI/CD pipelines

Good to Have :

– Exposure to AI/ML pipelines, MLOps, feature stores

– Experience with low-code/no-code or ETL platforms

– Knowledge of data governance, security, and compliance

Success Metrics :

– High-performance, reliable data pipelines in production

– Improved system scalability and processing efficiency

– Strong engineering practices and team mentorship

– Faster data delivery and improved developer productivity

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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