Description :

Role Summary :

We are seeking a Lead Data Engineer to design, build, and own scalable data platforms and pipelines that support analytics, reporting, and data-driven decision-making.

This role requires strong technical expertise, architectural thinking, and leadership capability.

You will guide data engineering practices, mentor engineers, and collaborate closely with analytics, data science, and product teams.

Experience : 7 to 10+ years

Department : Data / Engineering

Location : On-site / Hybrid / Remote (as applicable)

Key Responsibilities :

- Design and own scalable, reliable, and secure data architectures for batch and real-time processing.

- Define data platform standards, best practices, and long-term technical roadmap.

- Evaluate and implement tools for ingestion, processing, storage, and analytics.

- Build and maintain ETL/ELT pipelines for structured and unstructured data.

- Ensure high availability, performance, and data quality across pipelines.

- Implement data validation, monitoring, alerting, and recovery mechanisms.

- Lead data solutions on cloud platforms such as AWS, Azure, or GCP.

- Design data lakes and warehouses using tools like S3, BigQuery, Redshift, Snowflake, Synapse.

- Optimize cost, performance, and scalability of data infrastructure.

- Design and manage real-time data processing using Kafka, Kinesis, Pub/Sub, or similar.

- Support event-driven architectures and streaming analytics use cases.

- Define and enforce data governance, access controls, and security best practices.

- Ensure compliance with data privacy and regulatory standards.

- Implement metadata management, lineage, and data cataloging.

- Lead and mentor data engineers; conduct code reviews and technical guidance.

- Collaborate with analytics, BI, data science, and product teams to understand data needs.

- Translate business requirements into scalable technical solutions.

- Improve data engineering processes, automation, and reliability.

- Drive adoption of modern data engineering practices and tools.

- Evaluate emerging technologies to enhance platform efficiency.

Required Skills & Experience :

- Strong experience with Python, SQL, and data engineering frameworks.

- Hands-on expertise with ETL/ELT tools (Airflow, dbt, Glue, Dataflow, Azure Data Factory).

- Deep understanding of data modeling, schema design, and performance tuning.

- Extensive experience with cloud data ecosystems (AWS / Azure / GCP).

- Proficiency with data warehouses and lakehouse architectures.

- Experience with containerization and orchestration (Docker, Kubernetes) is a plus