Lead Data Engineer

Who We Are

NEXT Ventures is where ambition takes shape and movement becomes momentum. As a global platform revolutionising access to performance-based capital, we empower the world’s most driven individuals to rise. Through our flagship brand, FundedNext, we empower dreamers to become doers, and potential to turn into performance. With 500+ driven minds across five countries, we power a global rhythm—220,000+ daily users from 170+ nations, each chasing greatness in their own way.

Your Role in Our Mission

As Lead Data Engineer at NEXT Ventures, you'll own the data platform that powers every major decision—turning high-volume trading and product data into reliable, analytics-ready truth that drives revenue reporting, trader analytics, surveillance, and AI. This is a build-from-scratch mandate: you'll inherit terabytes across multiple products and a team hungry for senior leadership, then architect the full stack from source ingestion to warehouse, take pipelines from zero to production, and set the standard for how data engineering is done here. You'll also do it the modern way—working inside AI-assisted environments (Claude, Claude Code, MCP) to ship in a day what once took a week. If you treat AI as a force multiplier and can raise an entire team's bar on how it's used, this role is for you.

How You’ll Make an Impact

Build the Cloud Data Platform

• Architect and scale the end-to-end cloud data platform: source ingestion, transformation, warehouse, and the monitoring and quality layers that keep it trustworthy

• Lead the transition from batch-oriented systems to a scalable AWS-based streaming architecture

• Design and implement real-time data replication pipelines across MySQL, PostgreSQL, and Amazon Redshift for core products hosted in the cloud

• Work with high-volume, terabyte-scale data across NEXT's products, building for the scale we'll reach in three years, not just today

• Ensure scalability, security, cost-efficiency, and performance in every data solution you ship

AI-Powered Engineering and Productivity

• Work daily within AI-assisted development setups (Claude, Claude Code, and similar tools) to accelerate design, coding, debugging, and documentation, delivering critical work at 5 to 7x traditional speed

• Build and maintain MCP (Model Context Protocol) servers that connect our data systems (warehouses, dashboards, internal APIs) to AI assistants

• Develop AI-powered dashboards and analytics experiences: natural-language querying, automated insight generation, and LLM-driven reporting layers

• Apply prompt engineering and LLM API integration (Anthropic/Claude API or similar) to automate repetitive data engineering tasks such as pipeline scaffolding, schema documentation, data quality checks, and runbook generation

• Evaluate emerging AI tooling and champion adoption across the team where it improves speed, reliability, or insight quality

Design and Model the Data Warehouse

• Own data-warehouse schema design and dimensional modeling as a signature deliverable

• Turn messy, multi-source data into clean, performant, analytics-ready tables that BI, finance, and product teams can build on with confidence

• Migrate and optimize OLAP workloads to cloud warehouses, tuning relentlessly for performance, scalability, and cost at multi-terabyte scale

Move Data Reliably at Scale

• Build and operate ETL/ELT and Change Data Capture (CDC) pipelines that replicate and transform data reliably across heterogeneous systems

• Orchestrate automated, observable, reproducible workflows for daily data transformation and delivery (Airflow, Step Functions, or similar)

• Create reusable automation frameworks and CI/CD workflows for reliable deployments, leveraging AI assistance to build them faster and better documented

• Hold the line on data integrity, accuracy, and timely delivery through validation tooling and post-load quality checks

Ensure Operational Reliability

• Monitor and troubleshoot data pipelines with a focus on uptime and SLA compliance

• Implement automated monitoring and alerting (CloudWatch, Grafana, Datadog) and conduct regular ETL/ELT health checks across all data flows

• Establish and lead the incident-response process: acknowledge, triage, and resolve production alerts promptly, and build the on-call rotation the team runs on

• Perform Root Cause Analysis (RCA) and implement long-term improvements, using AI tooling to accelerate log analysis and incident investigation

• Maintain high standards of data quality, validation, lineage, and governance

• Continuously optimize pipelines for performance and cost, and keep SOPs, runbooks, and operational workflows current with AI-assisted documentation

Power Analytics, BI and AI

• Partner with BI, analytics, product, and business teams to translate business requirements into robust data models and real-time reporting layers

• Deliver dashboards and reporting (Metabase, Tableau, Power BI) that give the firm visibility into revenue, financial, and customer-experience metrics, progressively enhanced with AI-driven features

• Prepare clean, governed datasets and feature sources that make AI and machine-learning use cases possible

Lead, Own, Deliver

• Own pipelines end to end, from ingestion through transformation, deployment, monitoring, and incident response, with no hand-offs and no gaps

• Build, mentor, and lead the data engineering team, setting technical standards, coding practices, and architectural direction, and reviewing work

• Translate complex data into strategic recommendations and present findings to cross-functional leadership

• Operate independently in a fast-moving environment, owning outcomes rather than tasks

• Stay current on emerging data and AI technologies and drive integrations that strengthen the data ecosystem

What You Bring

• 8+ years of data engineering experience, building data pipelines, ETL/ELT, and data warehousing at scale

• Hands-on expertise with Change Data Capture (CDC) and real-time data replication across heterogeneous systems, backed by a solid understanding of data architecture and production platforms

• Strong programming skills in Python (required), with R a plus, and deep experience with modern ETL/ELT and orchestration tooling

• Deep experience with cloud data platforms, including AWS (primary) and Snowflake and/or Google Cloud, plus strong command of PostgreSQL, MySQL, and Amazon Redshift

• A proven track record of data-warehouse schema design and analytical data modeling, delivering clean, performant, analytics-ready data

• Demonstrated experience operating at terabyte and multi-terabyte scale, with a focus on performance, reliability, and cost

• Demonstrated AI fluency: comfortable working in Claude / Claude Code (or equivalent AI coding assistants) as a core part of the daily workflow, not just occasional chat usage

• Familiarity with LLM APIs and practical prompt engineering

• Team management experience and genuine end-to-end pipeline ownership: you've been accountable for pipelines in production, not just their design

• Willingness to establish and participate in 24/7 alert monitoring and on-call coverage for critical data operations

Your X-Factor

• You thrive in ambiguity. Handed a raw source and an unclear problem, you return a clean, modeled, production-ready pipeline—no hand-holding required.

• You treat data quality and business impact as one thought, not competing priorities. Every pipeline you build is both trustworthy and tied to a decision that matters.

• You build for AI as a first-class consumer. You see AI/ML and modern tooling (MCP, chat-with-your-data, auto-generated insights) as core to the platform's future—and architect with that in mind.

• You own outcomes, not just implementation. You're accountable for whether the business can trust and act on its data, not just whether the code ran.

• You bring an edge that compounds. Bonus points for hands-on MCP servers, AI-powered BI features, streaming (Kafka, Kinesis, Flink), CI/CD and IaC (GitHub Actions, Terraform), data engineering certs (AWS, Snowflake, GCP), or a background in fintech, trading, or brokerage.

Your Journey after Applying

• 30 minute HR interview with the Talent Acquisition team member

• 45 minute Problem Solving Interview (with talent acquisition team & department front line manager)

• 60-minute Bar Raiser Interview (with head of department & talent acquisition lead)

Why Join NEXT

At NEXT Ventures, we believe the right talent fuels breakthrough innovation. If you're driven to connect great minds with big ideas and want to shape the future of fintech, we’d love to meet you. Join our team of bold thinkers where technology meets transformation.

Apply now and be part of our journey — the future is calling, and it starts with you.