Lead Data Engineer

Description

We are looking for an experienced Lead Data Engineer to shape and scale modern data solutions for a growing organization. This role is ideal for a senior, detail-oriented individual who enjoys working in a dynamic environment, contributing both strategically and hands-on across engineering initiatives. The ideal candidate brings strong technical depth in cloud-based data platforms, a practical approach to data architecture, and the ability to guide engineers while partnering effectively with business stakeholders.

Responsibilities:
Lead the design, development, and maintenance of scalable data pipelines and warehouse solutions using Python, Snowflake, and Azure-based technologies.
Define data architecture standards that improve reliability, usability, and long-term scalability across the data environment.
Drive performance tuning and optimization efforts for data processing workflows, storage structures, and query execution.
Establish and strengthen data quality practices by implementing validation, monitoring, and governance measures across datasets and pipelines.
Serve as a senior technical leader who can step into managerial responsibilities, providing direction, feedback, and support to engineering team members.
Mentor engineers through code reviews, technical coaching, and knowledge sharing to elevate team capability and engineering consistency.
Partner closely with business teams and technical stakeholders to translate operational and analytical needs into effective data solutions.
Contribute hands-on to ETL development and cloud data platform enhancements, with exposure to Databricks considered an advantage.
Act as a key point of coordination between business priorities and technical execution to ensure data initiatives deliver measurable value.

Requirements

Extensive experience in data engineering, including leadership responsibilities within fast-moving or smaller organizational environments.
Strong programming skills in Python with proven success building and supporting production-grade data pipelines.
Hands-on expertise with Snowflake and Microsoft Azure data services.
Solid understanding of ETL processes, data warehousing concepts, and modern cloud data architecture.
Demonstrated knowledge of data performance optimization, data modeling, and data quality best practices.
Ability to mentor engineers and influence technical decisions across a broader team.
Excellent communication skills with the ability to bridge business objectives and technical implementation.
Experience with Azure Databricks is beneficial, though not required.

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