VentureDive

Senior Data Engineer

VentureDive

IT Services and IT Consulting · 201-500 employees

Yesterday
Senior (5-10 yrs) Full-time
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About the role

The role involves owning the data foundation for a banking AI/ML programme, including mapping existing warehouses and building transformation layers. You will maintain data infrastructure and feature engineering pipelines to support ML models and production activation.

What they look for

SQL Data Modelling ETL/ELT Pipeline Design Data Profiling Feature Engineering Data Lineage Data Governance Stakeholder Management AI Fluency Data Warehouse Navigation Data Quality Assessment Infrastructure Maintenance

Requirements

Requires over 5 years of data engineering experience with strong SQL skills and expertise in ETL/ELT and data modelling. Candidates must be proficient in using AI tools for workflow efficiency and be comfortable working independently at client sites.

Benefits

Competitive Salaries

Full description

Job Brief: We are looking for a Senior Data Engineer to join a banking AI/ML programme, working with the client's data and business teams alongside a partner consultancy. You will own the data foundation: understanding the client's existing data warehouse, assessing what is usable, building the analytics and ML-ready transformation layer, and maintaining data infrastructure from foundation through production and activation. This is a hands-on role where you operate independently, engage client stakeholders directly, and make judgment calls on data fitness before the wider delivery team ramps up.

VentureDive Overview: Founded in 2012 by veteran technology entrepreneurs from MIT and Stanford, VentureDive is the fastest-growing technology company in the region that develops and invests in products and solutions that simplify and improve the lives of people worldwide. We aspire to create a technology organization and an entrepreneurial ecosystem in the region that is recognized as second to none in the world.

Key Responsibilities• Map and understand the client's existing data warehouse and its source systems, including schemas, field definitions, data freshness, and known quality issues.

  • Assess data quality, completeness, and fitness for analytics and ML consumption across historical datasets.
  • Co-author and maintain the data dictionary with the client's data and analytics team, covering all fields, definitions, transformations, and lineage.
  • Design and build the analytics/ML-ready transformation layer on top of the existing warehouse, producing clean, feature-ready datasets for propensity and retention models.
  • Build and maintain feature engineering pipelines that serve the ML team with daily customer-level snapshots, behavioural aggregations, and derived signals.
  • Validate data refresh timing and batch dependencies to confirm daily scoring feasibility.
  • Support baseline metric extraction for product benchmarking from historical data.
  • Document data gaps, quality issues, and remediation paths within agreed timelines.
  • Coordinate with client IT on analytics environment provisioning, access, and infrastructure requirements.
  • Maintain data lineage, quality monitoring, and governance standards as the programme scales into production.
  • Support production data infrastructure, including pipeline reliability, monitoring, and continuous improvement through later programme phases.

Required Skills• 5+ years of hands-on data engineering experience.

  • Strong SQL skills and experience with data modelling, ETL/ELT pipeline design, and data transformations.
  • Experience navigating large enterprise data warehouses and understanding complex, multi-source schemas.
  • Data profiling and quality assessment across large historical datasets.
  • Feature engineering experience for ML and analytics use cases.
  • Solid understanding of data lineage, governance, and documentation practices.
  • Strong communication skills with the ability to engage client data teams, IT, and business stakeholders as peers.
  • Comfortable working independently on-site at client premises with minimal supervision.
  • AI fluency: uses Claude, ChatGPT, Cursor, GitHub Copilot, or similar tools as part of the daily workflow. Validates and refines AI-generated output rather than accepting it blindly.

Nice to Have• Banking or financial services domain experience.

  • Experience working with on-prem enterprise data platforms and infrastructure.
  • Exposure to regulatory or compliance-sensitive data environments.
  • Experience building data layers that serve real-time or daily-batch ML scoring systems.
  • Familiarity with data governance in regulated industries.

What we look for beyond required skills In order to thrive at VentureDive, you …are intellectually smart and curious …have the passion for and take pride in your work …deeply believe in VentureDive’s mission, vision, and values …have a no-frills attitude …are a collaborative team player …are ethical and honest

Are you ready to put your ideas into products and solutions that will be used by millions? You will find VentureDive to be a quick pace, high standards, fun and a rewarding place to work at. Not only will your work reach millions of users world-wide, you will also be rewarded with competitive salaries and benefits. If you think you have what it takes to be a VenDian, come join us ... we're having a ball! #LI-Hybrid