Technical Sales Engineer jobs in United States, Florida, Miami

Technology – Data and Decision Science – Data Engineering – Senior

In this role, you will design and build analytics solutions that deliver significant business value. You will collaborate with other data and analytics professionals, management, and stakeholders to ensure that technical requirements align with business needs. Your responsibilities will include creating scalable data architecture and modeling solutions that support the entire data asset lifecycle.

Your key responsibilities

As a Senior Data Engineer, you will play a pivotal role in transforming data into actionable insights. Your time will be spent on various responsibilities, including:

• Designing, building, and operating scalable on-premises or cloud data architecture.
• Analyzing business requirements and translating them into technical specifications.
• Optimizing data flows for target data platform designs.
• Design, develop, and implement data engineering solutions using Databricks on cloud platforms (e.g., AWS, Azure, GCP).
• Collaborate with clients to understand their data needs and provide tailored solutions that meet their business objectives.
• Lead end-to-end data pipeline development, including data ingestion, transformation, and storage.
• Ensure data quality, integrity, and security throughout the data lifecycle.
• Provide technical guidance and mentorship to junior data engineers and team members.
• Communicate effectively with stakeholders, including technical and non-technical audiences, to convey complex data concepts.
• Manage client relationships and expectations, ensuring high levels of satisfaction and engagement.
• Stay updated with the latest trends and technologies in data engineering and cloud computing.

This role offers the opportunity to work with cutting-edge technologies and stay ahead of industry trends, ensuring you gain a competitive advantage in the market. The position may require regular travel to meet with external clients.

Skills and attributes for success

To thrive in this role, you will need a blend of technical and interpersonal skills. Your ability to communicate effectively and build relationships will be crucial. Here are some key attributes we look for:

• Strong analytical and decision-making skills.
• Proficiency in cloud computing and data architecture design.
• Experience in data integration and security.
• Ability to manage complex problem-solving scenarios.

To qualify for the role, you must have

• A Bachelor’s degree in Computer Science, Engineering, or a related field required (4-year degree). Master’s degree preferred
• Typically, no less than 2 - 4 years relevant experience in data engineering, with a focus on cloud data solutions.

• 5+ years of experience in data engineering, with a focus on cloud data solutions.
• Expertise in Databricks and experience with Spark for big data processing.
• Proven experience in at least two end-to-end data engineering implementations, including:

• Implementation of a data lake solution using Databricks, integrating various data sources, and enabling analytics for business intelligence.

• Development of a real-time data processing pipeline using Databricks and cloud services, delivering insights for operational decision-making.

• Strong programming skills in languages such as Python, Scala, or SQL.
• Experience with data modeling, ETL processes, and data warehousing concepts.
• Excellent problem-solving skills and the ability to work independently and as part of a team.
• Strong communication and interpersonal skills, with a focus on client management.

Required Expertise for Senior Consulting Projects:

• Strategic Thinking: Ability to align data engineering solutions with business strategies and objectives.
• Project Management: Experience in managing multiple projects simultaneously, ensuring timely delivery and adherence to project scope.
• Stakeholder Engagement: Proficiency in engaging with various stakeholders, including executives, to understand their needs and present solutions effectively.
• Change Management: Skills in guiding clients through change processes related to data transformation and technology adoption.
• Risk Management: Ability to identify potential risks in data projects and develop mitigation strategies.
• Technical Leadership: Experience in leading technical discussions and making architectural decisions that impact project outcomes.
• Documentation and Reporting: Proficiency in creating comprehensive documentation and reports to communicate project progress and outcomes to clients.

Ideally, you’ll also have

• Experience with data quality management.
• Familiarity with semantic layers in data architecture.
• Familiarity with cloud platforms (AWS, Azure, GCP) and their data services.
• Knowledge of data governance and compliance standards.
• Experience with machine learning frameworks and tools.

Technology – Data and Decision Science – Data Engineering – Manager

In this role, you will design and build analytics solutions that deliver significant business value. You will collaborate with other data and analytics professionals, management, and stakeholders to ensure that business requirements are translated into effective technical solutions. Key responsibilities include:

• Understanding and analyzing business requirements to translate them into technical requirements.
• Designing, building, and operating scalable data architecture and modeling solutions.
• Staying up to date with the latest trends and emerging technologies to maintain a competitive edge.

Key Responsibilities:

As a Data Engineering Manager, you will play a crucial role in managing and delivering complex technical initiatives. Your time will be spent across various responsibilities, including:

• Leading workstream delivery and ensuring quality in all processes.
• Engaging with clients on a daily basis, actively participating in working sessions, and identifying opportunities for additional services.
• Implementing resource plans and budgets while managing engagement economics.

This role offers the opportunity to work in a dynamic environment where you will face challenges that require innovative solutions. You will learn and grow as you guide others and interpret internal and external issues to recommend quality solutions. Travel may be required regularly based on client needs.

Skills and attributes for success:

To thrive in this role, you should possess a blend of technical and interpersonal skills. The following attributes will make a significant impact:

• Lead the design and development of scalable data engineering solutions using Databricks on cloud platforms (e.g., AWS, Azure, GCP).
• Oversee the architecture of complex cloud analytics solutions, ensuring alignment with business objectives and best practices.
• Manage and mentor a team of data engineers, fostering a culture of innovation, collaboration, and continuous improvement.
• Collaborate with clients to understand their analytics needs and deliver tailored solutions that drive business value.
• Ensure the quality, integrity, and security of data throughout the data lifecycle, implementing best practices in data governance.
• Drive end-to-end data pipeline development, including data ingestion, transformation, and storage, leveraging Databricks and other cloud services.
• Communicate effectively with stakeholders, including technical and non-technical audiences, to convey complex data concepts and project progress.
• Manage client relationships and expectations, ensuring high levels of satisfaction and engagement.
• Stay abreast of the latest trends and technologies in data engineering, cloud computing, and analytics.
• Strong analytical and problem-solving abilities.
• Excellent communication skills, with the ability to convey complex information clearly.
• Proven experience in managing and delivering projects effectively.
• Ability to build and manage relationships with clients and stakeholders.

To qualify for the role, you must have:

• Bachelor’s degree in computer science, Engineering, or a related field required; Master’s degree preferred.
• Typically, no less than 4 - 6 years relevant experience in data engineering, with a focus on cloud data solutions and analytics.
• Proven expertise in Databricks and experience with Spark for big data processing.
• Strong background in data architecture and design, with experience in building complex cloud analytics solutions.
• Experience in leading and managing teams, with a focus on mentoring and developing talent.
• Strong programming skills in languages such as Python, Scala, or SQL.
• Excellent problem-solving skills and the ability to work independently and as part of a team.
• Strong communication and interpersonal skills, with a focus on client management.

Required Expertise for Managerial Role:

• Strategic Leadership: Ability to align data engineering initiatives with organizational goals and drive strategic vision.
• Project Management: Experience in managing multiple projects and teams, ensuring timely delivery and adherence to project scope.
• Stakeholder Engagement: Proficiency in engaging with various stakeholders, including executives, to understand their needs and present solutions effectively.
• Change Management: Skills in guiding clients through change processes related to data transformation and technology adoption.
• Risk Management: Ability to identify potential risks in data projects and develop mitigation strategies.
• Technical Leadership: Experience in leading technical discussions and making architectural decisions that impact project outcomes.
• Documentation and Reporting: Proficiency in creating comprehensive documentation and reports to communicate project progress and outcomes to clients.

Large-Scale Implementation Programs:

1. Enterprise Data Lake Implementation: Led the design and deployment of a cloud-based data lake solution for a Fortune 500 retail client, integrating data from multiple sources (e.g., ERPs, POS systems, e-commerce platforms) to enable advanced analytics and reporting capabilities.
2. Real-Time Analytics Platform: Managed the development of a real-time analytics platform using Databricks for a financial services organization, enabling real-time fraud detection and risk assessment through streaming data ingestion and processing.
3. Data Warehouse Modernization: Oversaw the modernization of a legacy data warehouse to a cloud-native architecture for a healthcare provider, implementing ETL processes with Databricks and improving data accessibility for analytics and reporting.

Ideally, you’ll also have:

• Experience with advanced data analytics tools and techniques.
• Familiarity with machine learning concepts and applications.
• Knowledge of industry trends and best practices in data engineering.
• Familiarity with cloud platforms (AWS, Azure, GCP) and their data services.
• Knowledge of data governance and compliance standards.
• Experience with machine learning frameworks and tools.