Data Migration Consultant
RidgelineReno, NV; San Ramon, CA$80k – $94kPosted 27 March 2026
Job Description
Are you an early- to mid-career professional with a passion for delivering impactful software implementations? Do you enjoy solving complex data challenges and ensuring accuracy and integrity at every stage of the process? Are you eager to collaborate with cross-functional teams to transform how investment management firms operate and scale? If so, we invite you to be a part of our innovative team.
As a Data Migration Consultant at Ridgeline, you will support end-to-end implementations of Ridgeline’s enterprise cloud platform for investment management customers. You will partner closely with internal teams and clients to deliver high-quality data migration solutions, with a focus on investment, performance, and transactional data. You’ll take a consultative approach to problem-solving while helping ensure successful go-lives and long-term customer success. In this role, you will leverage cutting-edge technologies—including AI tools like ChatGPT—to enhance efficiency, improve data accuracy, and streamline implementation workflows.
At Ridgeline, how we work matters as much as what we build. Ridgeliners act like owners, choose growth over comfort, and communicate with transparency. We assume positive intent, bias toward action, and bring solutions—not just problems. We celebrate wins, learn from setbacks, and thrive in a resilient, collaborative, high-performing culture. If this excites you, we’d love to meet you.
You must be work authorized in the United States without the need for employer sponsorship.
The impact you will have:
Support data conversion projects from inception to completion, ensuring data integrity, accuracy, and accessibility
Collaborate with implementation teams and customers to gather requirements and understand portfolio accounting and performance data needs
Perform data mapping, transformation, and cleansing activities aligned with project specifications
Validate and troubleshoot variances between source data and system-generated outputs to ensure consistency
Partner with project managers and functional consultants to proactively identify dependencies and drive alignment across workstreams
Contribute to improving processes by partnering with engineering teams to enhance automation and efficiency
Deliver clear and timely communication to customers and internal stakeholders regarding data migration progress and outcomes
Apply Ridgeline’s implementation methodology to provide holistic, client-focused solutions
Take ownership of deliverables and proactively identify risks while driving toward resolution
Foster a collaborative, transparent team environment that promotes learning and continuous improvement
Contribute to broader Customer Success initiatives to enhance the onboarding experience and long-term client satisfaction
What we look for:
1–3 years of experience in data conversion or migration projects within the investment management industry
Strong technical skills, including experience with SQL
Advanced proficiency in Microsoft Excel
Knowledge of financial instruments, asset management workflows, and reconciliation/reporting processes
Familiarity with portfolio accounting systems and performance measurement methodologies across asset classes
Experience working on complex data projects from planning through execution
Excellent problem-solving skills and strong attention to detail
Strong communication and client-facing skills with the ability to translate technical concepts clearly
Ability to work both independently and collaboratively while managing multiple priorities
Willingness to learn and apply cutting-edge technologies in a dynamic environment
An aptitude for problem solving and continuous improvement
Ability to communicate effectively across technical and non-technical audiences
Serious interest in having fun at work
Bonus:
Basic understanding of AWS, APIs, and serverless technologies
Familiarity with Agile methodologies and software development lifecycles
CFA or CPA ... (truncated, view full listing at source)
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