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Key skills for this role
Design, build, and optimize scalable, secure, and repeatable data pipelines using AWS (S3, Glue, Lambda, Step Functions, Redshift, IAM) and Databricks ( Py Spark , Lakeflow , Delta Lake, Unity Catalog).
Serve as the technical leader for data ingestion pipelines, modeling new datasets such as Advisor360, CRM, ETF, and Mutual Fund platforms.
Apply strong data modeling (dimensional, canonical, and domain-driven) principles to support analytics, reporting, and AI/ML use cases.
Ensure alignment with enterprise data architecture standards, promoting reusability, governance, and long-term maintainability.
With limited guidance, independently perform deep investigations, identify data issues, and propose solutions that balance performance, cost, risk, and business needs.
Engage business stakeholders to gather ambiguous requirements, ask the right questions, and translate them into clear technical designs.
Provide thought leadership and recommend technical patterns, frameworks, and toolsets.
Implement robust data quality frameworks , monitoring, and alerting to ensure high trust in business-critical data assets.
Troubleshoot data inconsistencies and ensure proper logging, testing, and recovery mechanisms across pipelines.
Lead regression testing, software upgrades, and production deployments with strong change control discipline.
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Manchester, GBR
London, GBR
Manchester, GBR
Malvern, USA
Malvern, USA
Malvern, USA
Malvern, USA
Lead all phases of solution development—from design to deployment and operationalization.
Mentor and guide other engineers in coding standards, architecture patterns, Databricks best practices, and AWS platform usage.
Partner with Data Architecture, Analytics, Product, and Business teams to deliver solutions that improve decision-making.
Provide training sessions and documentation to uplift the data engineering maturity across the organization.
Participate in strategic initiatives such as AI readiness, data unification efforts, metadata strategy, and enterprise integration roadmaps.
Drive continuous improvement in engineering frameworks, onboarding workflows, and platform capability.
Required Skills:
5 + years of experience in data engineering, data architecture, or large-scale distributed data systems.
Expert-level experience with cloud platforms such as AWS, GCP, or Azure, leveraging services for data storage, ingestion, pipeline orchestration, database or lake house management, data transformation .
5+ years of hands-on experience designing, developing, and supporting enterprise-scale data pipelines on the Databricks Lakehouse platform using PySpark , Delta Lake, Databricks Workflows, and Lakeflow Declarative Pipelines
Strong background in data modeling (dimensional, canonical, data vault, or domain-driven).
Proven ability to work independently with minimal direction and deliver high-quality solutions in ambiguous environments.
Demonstrated experience translating complex business problems into scalable technical solutions.
Strong SQL and Python skills, with emphasis on ETL/ELT pipeline development.
Experience with CI/CD, GitHub, DevOps workflows, and automated testing.
Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.
Global investment management firm owned by its client funds.
Visit company websiteJobs and hiring trendsCAD 90000-140000 yearly / year
Full-time
Senior · 5+ years experience
Hybrid
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