Job Specifications
Job Description
We are hiring 2 Salesforce Data Engineers for one of our Consulting clients in the Financial Services industry
This is a remote contract and can be based from any nearshore/LATAM location - the customer is based in the US and working hours will be EST.
The contract is 40 hours/week starting from the beginning of March for at least 2 months, with potential for extension - paid at an hourly rate of $25-30/h
Professional English proficiency is required -
This person will build and maintains the systems that power AI, analytics, and data driven decision making. This role focuses on creating and orchestrating efficient data pipelines, organizing data for scale, and ensuring data is clean, secure, and ready for use across the business. The work supports BI, Operations, System Integrations and AI practices by ensuring high quality data is consistently available.
Primary Responsibilities
Design, build, and maintain data pipelines that support efficient collection, ingestion, storage, and processing
Implement modern data architectures such as, data lakes, data warehouses, lakehouses, and data mesh platforms
Develop streaming data flows for near real time and low latency use cases
Clean and prepare data to support analytics, reporting, and AI model readiness
Improve performance and reliability across data systems
Apply data governance and security best practices to safeguard customer information
Partner with technical and business teams to understand requirements and deliver effective solutions
Identify opportunities to streamline operations and reduce cost through smarter data design
Monitor and resolve issues to maintain dependable, resilient data operations
Required Qualifications
Experience building and maintaining data pipelines, and ETL/ELT scalable frameworks.
Experience in Salesforce projects handling data migrations and integrations within the platform
Strong foundation in relational and non relational data systems
Strong data modeling skills
Working knowledge of data lake, data warehouse, and lakehouse patterns
Hands on experience with both batch and streaming data pipelines
Proficiency in SQL, Python and modern data engineering tools and libraries, such as Pandas
Ability to design structured, scalable solutions for analytics and AI preparation
Familiarity with cloud platforms and distributed processing frameworks
Clear, concise communication skills
Experience with Databricks, Snowflake, Microsoft Synapse, Fabric, AWS Glue, DMS, or similar data platforms and technologies
Experience with Open Data platforms and tools, such as Apache Spark, Airflow, Delta Lake, or Iceberg
Background supporting Data migrations, API integrations, and Machine Learning or AI data requirements
Understanding of data governance, lineage, and secure data practices
Exposure to a data product mindset and domain oriented or data mesh approaches
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About the Company
In late 2009, Stott and May was founded on the belief that hiring managers should never have to make the choice between time to hire and quality of candidate. We noticed a gap in the market for a search offering that can combine the speed of contingent recruitment with a high value, high touch, insightful service more commonly found in the executive search space. A talent solution that's helpful, engaging, collaborative and stacked full of value-add. Built with the flexibility to provide our partners with a choice of best pr...
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