• Design and maintain Snowflake data models for customer engagement and audience activation • Build customer-level data structures for segmentation and lifecycle marketing • Create and maintain data pipelines for syncing with downstream tools • Support clients in building customer 360 views across channels • Ensure data quality and compliance in customer-facing data models

Role Description We're partnering with an innovative and fast-growing organisation looking to strengthen its data function with the addition of a Lead Data Engineer. This is an exciting opportunity for an experienced data professional to take a leading role in designing, building and optimising modern data platforms that power business intelligence, analytics and strategic decision-making. You'll combine hands-on engineering with technical leadership, helping to shape the organisation's data capabilities while working closely with stakeholders across the business. This role would suit someone who enjoys solving complex data challenges, driving best practice and mentoring others, while remaining technically involved in delivering high-quality data solutions. - Lead the design, development and optimisation of scalable data pipelines and cloud-based data platforms - Build and maintain robust ETL/ELT processes to support reporting and analytics - Drive improvements in data quality, reliability, performance and governance - Collaborate with technical and business stakeholders to understand and deliver data requirements - Provide technical leadership and mentoring within the data engineering team - Support architectural decisions and contribute to the ongoing evolution of the organisation's data platform - Identify opportunities to automate processes and improve data engineering best practices - Play a key role in delivering business-critical data projects from concept through to deployment Qualifications - 5+ years' commercial experience working as a Data Engineer - Previous experience leading projects or providing technical leadership within a data engineering team - Strong SQL and Python skills - Commercial experience with Databricks - Experience using Airflow and/or Azure Data Factory - Experience working with dbt - Strong understanding of data warehousing and modern data engineering principles - Experience building and maintaining scalable cloud-based data platforms and pipelines - Excellent problem-solving, communication and stakeholder management skills Benefits - Fully remote working within the UK - Competitive salary of 70,000 - 80,000 - Comprehensive benefits package - Opportunity to lead impactful data engineering initiatives - Exposure to modern cloud technologies and large-scale data projects - Flexible working environment - Excellent opportunities for professional development and career progression

Role Description Design, build, and maintain scalable data infrastructure and pipelines that support banking operations, analytics, and regulatory compliance. This role bridges technical leadership with strategic business needs, ensuring data is accurate, secure, and accessible. Key Responsibilities - Data Architecture & Pipeline Development - Design and implement robust, scalable data pipelines for structured and unstructured data. - Oversee ETL/ELT processes to ingest data from core banking systems, CRM, and external sources. - Ensure data models support real-time analytics, reporting, and machine learning. - Collaborate with data scientists and analysts to provide usable, cleaned and aggregated data to end users. - Governance, Security & Compliance - Ensure data practices comply with banking regulations (e.g., Basel III, GDPR, local laws). - Implement data governance frameworks, including metadata management and lineage tracking. - Collaborate with cybersecurity teams to safeguard sensitive financial data. - Performance Optimization - Monitor and optimize data infrastructure for performance, reliability, and cost-efficiency. - Troubleshoot data issues and ensure high availability of data services. - Innovation & Tooling - Evaluate and adopt modern data technologies (e.g., cloud platforms, data lakes, streaming tools). - Promote automation and DevOps practices in data engineering workflows. Qualifications - Bachelor’s or Master’s in Computer Science, Data Engineering, or related field. - At least 7 years in data engineering, with 2+ years in a leadership role. - Strong experience with SQL, Python/Scala, Spark, Kafka, and cloud platforms (AWS/GCP/Azure). - Familiarity with banking systems, financial data structures, and regulatory requirements. - Excellent communication and stakeholder management skills.

• Define the overall migration strategy, scope, approach (phasing/co-existence if applicable), and acceptance criteria; align with program releases and cutover milestones. • Develop and maintain the data migration plan (timeline, environments, mock runs/dress rehearsals, final cutover), including dependencies with Integration, Testing, Operations, and legacy platform teams. • Govern vendor delivery: review and validate vendor migration design, runbooks, ETL approach, mapping specifications, transformation rules, and reconciliation logic; ensure clear RACI and delivery ownership. • Coordinate with banks business and IT SMEs to validate source-to-target mappings, reference data alignment, and critical business rules (e.g., product parameters, balances, interest, fees, account status). • Define and execute banks verification controls with the vendor (record counts, control totals, balance/GL tie-outs, exception management) and obtain stakeholder sign-off for each migration cycle. • Lead migration defect triage from a banks perspective: ensure issues are logged, prioritized, tracked to closure, and that RCA is completed for recurring defects; escalate risks early. • Ensure test data readiness for SIT/UAT and parallel run activities (data refreshes, data masking where required, refresh cadence, and data-related test support). • Plan and coordinate cutover data activities and readiness checkpoints (pre-cutover snapshots, load sequencing, reconciliation checkpoints, rollback/fallback approach) with minimal downtime and operational impact. • Ensure compliance with banks data governance, security, and regulatory requirements throughout migration (access control, secure transfer, audit trail, retention and masking for non-production). • Provide regular status reporting (progress, risks, issues, decisions) to program governance and senior stakeholders; maintain RAID and action logs for the data migration workstream.