Lead Data Engineer

We’re working with a fast-growing, engineering-first consultancy that’s redefining how modern data solutions are designed and delivered. They partner with some of the biggest names in finance, media, and tech to build intelligent, scalable, cloud-native data platforms. Now, they're looking to bring on a Lead Data Engineer —a hands-on leader fluent in both the architecture and the execution of cutting-edge data systems.

This isn’t a manager-in-a-meeting kind of role. They’re looking for a "keyboard-first" engineer—someone who can set the strategy and implement it, mentor teams, and drive results with modern tools like Databricks , Delta Lake , Spark , and Airflow .

? What You’ll Do

• Architect & Lead: Design and implement scalable, high-performance data solutions using Databricks, Delta Lake, and modern ETL/ELT tooling.

• Collaborate Cross-Functionally: Work closely with engineers, analysts, and business teams to deeply understand requirements and deliver the right data infrastructure.

• Mentor & Guide: Provide technical leadership and mentoring for data engineers, helping raise the bar on the team’s output and capability.

• Drive Best Practices: Implement a DataOps mindset; build resilient, automated data pipelines that adhere to best practices in security, governance, and performance.

• Share Knowledge: Lead from the front—not just in projects, but by contributing to blog posts, internal wikis, and other engineering-wide initiatives.

? What You Bring

• 10+ years of experience in data engineering, architecture, or related fields

• Expertise in Databricks and large-scale data lakes (Delta Lake experience preferred)

• Strong programming skills (e.g. Python, Scala) and comfort with Spark

• Deep understanding of ETL/ELT , data modeling, and cloud-native data solutions (e.g. AWS Redshift, Azure Synapse, Google BigQuery)

• Hands-on experience with data orchestration tools like Apache Airflow

• Strategic mindset and analytical skills—able to design and defend solutions at a high level, then get in the weeds to build them

• Excellent communication skills—capable of interacting with both engineers and non-technical stakeholders

? Tech & Tooling You’ll Use

• Languages: Python, SQL, Scala

• Frameworks: Databricks, Delta Lake, Apache Spark

• Cloud & Storage: AWS (preferred), Azure, GCP; Redshift, BigQuery, Synapse

• Tooling: Airflow, Git, Terraform (bonus)

• Mindset: DataOps, CI/CD, reusable architecture, scalable systems

? Why Join?

• Collaborate with elite engineers and innovators across industries like finance and tech

• Influence enterprise-scale systems at companies you already know

• Be part of a community, not a hierarchy—where ideas win, not titles

• Enjoy flexibility, growth, and hands-on learning with people who take craft seriously

? Interview Process

• Technical assessment (GitHub-submitted take-home)

• Technical & architecture interviews with engineering leads

• Culture interview (optional for fast-tracked candidates)

• Timeline: Can move quickly—most offers go out within 7–10 days