400+ experienced team players

Scale Your Data Team: Hire Databricks Engineers

Expertise when you need it. Centralize data sources for processing, storage, analysis, and monetization–from BI to Generative AI solutions.

Powering Innovations at 350+ Companies Worldwide

Try Before You Buy: 100% Risk-Free Trial

Our objective is to streamline your development process. Therefore, we offer the opportunity to trial our candidates to ensure technical and cultural fit at no financial risk to you.

Only Certified Staff

All developers, engineers, project managers, and technology experts are Sphere’s full-time employees. This enables us to swiftly provide you with vetted talent—in days, not weeks.

Global Talent Pool at Your Fingertips

Our extensive pool of engineers and developers stands ready to bolster your team and tackle even the most daunting challenges. Please meet some of our selected team members below.

Aron
Aron Data Architect
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Europe

Experienced Databricks Developer with a strong background in big data analytics and cloud technologies. Skilled in designing and implementing scalable data solutions using Databricks platform, in building data pipelines and ETL processes. Seeking a senior-level role to leverage expertise in data engineering and machine learning to drive business insights and innovation.

TECHNOLOGIES
Programming languages/TechnologiesDatabricksApache SparkPython PySpark SQLScalaDelta LakeSnowflakeCI/CDGitLab CIFrameworks/LibrariesTensorFlow, scikit-learnPlatformsAWSAzureCertificationsDatabricks Certified DeveloperAWS Certified Big Data - Specialty
PROJECT HIGHLIGHTS Designed and implemented real-time data processing pipelines using Databricks and Apache Spark, resulting in improved data quality and faster insights. Leveraged machine learning algorithms to develop predictive analytics models for business use cases. Collaborated with cross-functional teams to understand data requirements and deliver scalable solutions. Optimized data workflows and implemented performance tuning techniques to enhance processing speed.
Mak
Mak Data Engineer
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Europe

Seasoned Databricks Developer with a proven track record of delivering high-performance data solutions. Experienced in architecting and implementing complex data pipelines on cloud platforms. Seeking a senior role to drive data-driven decision-making and optimize business processes.

TECHNOLOGIES
Programming languages/TechnologiesDatabricksApache SparkPySpark SQLPythonScalaDocker, KubernetesFlinkTableauSnowflakeJenkinsKafkaPlatformsAWSAzureCertificationsAWS Certified Cloud Practitioner
PROJECT HIGHLIGHTS Designed and implemented scalable data architectures using Databricks and Apache Spark to process petabyte-scale datasets. Integrated Databricks with other cloud services for seamless data ingestion and analytics. Implemented data governance and security best practices in Databricks environments. Led performance optimization initiatives to enhance data processing efficiency. Architected and deployed Databricks solutions for real-time analytics and machine learning applications. Collaborated with data scientists to develop and deploy advanced analytics models on Databricks clusters. Provided technical guidance and mentorship to junior developers on Databricks best practices.
Emma
Emma Data Scientist
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Detail-oriented developer with hands-on experience in building data pipelines and analytics solutions. Seeking a challenging role to further develop skills in big data technologies and contribute to impactful projects.

TECHNOLOGIES
Programming languages/TechnologiesApache SparkPySparkDatabricksTableauPower BISQLPythonPlatformsAzureAWS
PROJECT HIGHLIGHTS Developed and maintained ETL processes using Databricks for data transformation and cleansing.
Implemented batch and streaming data processing pipelines to support real-time analytics.
Collaborated with data analysts to visualize insights derived from Databricks-based data workflows.
Contributed to the optimization of data infrastructure and performance tuning efforts.
Designed and implemented Databricks jobs to process and analyze large datasets for business intelligence purposes.
Participated in data modeling discussions and contributed to the design of data warehouse schemas.
Rita Ginsburg
Rita Ginsburg Director of Global HR
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Ms. Ginsburg brings 12 years of corporate experience to Sphere and is well-versed in the day-to-day HR and business administration activities as well as strategic initiatives and long-term planning. Rita has transformed the Sphere HR function and, more importantly, its people into a competitive differentiator.

Alex Perez
Alex Perez Senior Client Partner
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Alex is an accomplished Senior Sales Executive with a successful track record working with S&P 500 organizations. He excels in navigating complex sales cycles, building, and nurturing long-term client relationships, and delivering tailored solutions that align with the unique needs of large organizations. His deep understanding of the enterprise market and his relentless pursuit of excellence have made him a trusted partner for driving and achieving revenue growth and achieving their business objectives.

Andrew Abbotsford-Smith
Andrew Abbotsford-Smith Sales Director
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As a Sales Director at Sphere Partners, a global technology consulting and digital innovations company, Andrew leverages his 20+ years of experience in the banking, financial services, and energy sectors to deliver generative AI solutions to clients across Europe and the Middle East. With a proven track record of driving new revenue and market opportunities, Andrew has extensive knowledge and expertise in the field of AI since 2006. Additionally, he has completed an AI Business course at MIT. Prior to joining Sphere Partners, Andrew held senior roles at Infosys, NICE Actimize, OneSpan, and Oracle.

Engagement Models Tailored to Your Business

Staff Augmentation: This model offers custom teams equipped with specialized skills, operating under the direct management and integration within our clients’ in-house teams. It ensures you maintain complete control over the project delivery process.

Managed Teams: Obtain a fully equipped, organized, and ready-to-work software development team with the necessary tech stack. This team will be dedicated to your business and managed by Sphere, ensuring seamless integration and productivity.

Scope-based Initiatives: Define your business or product objectives and accelerate development with a set timeline, scope and budget using our dedicated in-house teams standing at the ready.

EOR/CM Services: Streamline the process of hiring employees in new countries without the need to establish a legal entity, by leveraging Sphere as your Employer of Record.

Our Databricks Services

Databricks Consulting

Offering you the opportunity to scale AI, we can set up cloud and modern analytics tools to scale the use of data science and AI throughout your business. Drive change to enhance customer and employee experience with a robust data system. Move your proof of concepts into production faster through expert Databricks consulting.

Data Architecture

We build flexible data architectures using Databricks that promote the use of high quality, relevant, and accessible data. These cloud solutions help reduce costs and improve efficiencies and are built to grow along with your business.

Proof-of-Concept

Our advanced Proof-of-Concepts help you to evaluate the perks Databricks would bring to your business.

Custom Approach

We tailor our strategies to your specific project requirements to ensure your Databricks implementation starts on the right track.

Databricks Health Check

Sphere evaluates your existing Databricks environment for operational excellence, security, reliability, performance efficiency, and cost optimization. We’ll provide detailed recommendations and guiding best practices to improve on these five areas.

Database Platform Migration

Migrate your data assets to Databricks. Your custom migration plan will include stand-up and configuration of Databricks technical migration details for all environments, training, and go-live procedures.

Sphere’s Strategic Staffing Process

With over 19 years of successful track record in staff augmentation, Sphere offers a straightforward yet effective formula for success. We boast an extensive in-house talent network of more than 400 full-time engineering and development professionals worldwide, ready to embark on your project within 4-7 days.

Discovery Call

Consult with our expert team to identify and align on your objectives, technical requirements, and business needs

Hand-Selection

Obtain an initial list of candidates professionally matched to your project, ready for interview.

Confirmation

Confirm your new team members or adjust the selection criteria.

Onboarding

Onboard professionals you prefer to achieve your business goals.

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Ready to interview your next developer? Speak to our experts

Why Choose Sphere’s Talent?

Every project is unique, and we understand the importance of finding the right professional with the appropriate technological and cultural background, as well as an understanding of the technologies and methodologies used. That’s exactly what we do — match your tasks with the ideal candidates to make onboarding as swift as possible.

Furthermore, if you are not satisfied with the selected candidates, you can easily swap your resources.

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Join 350+ Satisfied Clients

Sphere engineers possess experience across dozens of industries and have a deep understanding of specific business requirements. But why take our word for it? Allow us to share successful case studies with you.

Gett

Gett aimed to expand its B2B offerings in the on-demand mobility space and needed to improve its initial slow and non-scalable system. In partnership with Sphere, they developed a microservices architecture using modern technologies like Golang, PostgreSQL, and Redis, resulting in a high-performance, scalable platform. This transformation allowed Gett to efficiently enter new markets and scale its operations.

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CreditNinja

CreditNinja decided to create an in-house system for processing and purchasing leads to better manage and reduce their underwriting and marketing costs. This system provided their data and marketing teams with significant control and adaptability in handling leads. By partnering with Sphere, CreditNinja developed a customized platform that not only cut customer acquisition costs by four times but also reduced underwriting expenses by 75% and marketing costs by 50%.

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Proclivity Systems

Proclivity Systems is a leading, smart advertising platform that connects brands to healthcare professionals and patients. We worked with Proclivity Systems to help create their core LayerRx platform; utilizing our comprehensive analytics and engineering experience.

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Accelerate Your Project Delivery

Streamline your development process and focus on what matters most. With Sphere, integrate expert developers into your team in just 4 days and start scaling efficiently.

Frequently Asked Questions about Databricks

Databricks is a cloud-based platform designed for massive scale data engineering and collaborative data science. It’s built on top of Apache Spark, which is an open-source, distributed computing system that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. Databricks streamlines the process of working with Spark by providing a managed environment that simplifies configuration and deployment. Here’s what Databricks is commonly used for:

  1. Big Data Processing and Analytics: Databricks allows users to process vast amounts of data quickly using Spark. It’s ideal for tasks like ETL (Extract, Transform, Load) processes, real-time data analytics, and complex data processing pipelines.
  2. Collaborative Data Science: The platform supports collaborative data science workflows, enabling data scientists to work together in real time. Users can explore data, share insights, and build models using a variety of programming languages, including Python, R, Scala, and SQL.
  3. Machine Learning: Databricks includes MLflow, a platform for managing the machine learning lifecycle, including experimentation, reproducibility, and deployment. This allows data scientists to streamline the creation, training, and deployment of machine learning models.
  4. Data Warehousing and Lakehouse: It provides a unified analytics platform that combines the benefits of data lakes and data warehouses, often referred to as a “lakehouse”. This architecture allows for cost-effective storage of big data while supporting analytics and machine learning on the same platform.
  5. Stream Processing: Databricks supports real-time stream processing, allowing users to analyze and respond to data as it arrives. This is crucial for applications that require immediate insights, such as fraud detection and live dashboards.
  6. BI Integration: The platform can connect to popular business intelligence tools, enabling organizations to create rich, interactive visualizations and reports directly from their data within Databricks.

Key features include collaborative notebooks for data science and engineering, a scalable Apache Spark environment, MLflow for machine learning lifecycle management, Delta Lake for reliable data storage, and integrated workflows for ETL processes and analytics.

Yes, Databricks is highly suitable for machine learning projects. It includes MLflow, a platform to manage the machine learning lifecycle, including experimentation, deployment, and reproducibility, as well as built-in collaboration tools for data scientists.

Databricks supports multiple programming languages, including Python, R, Scala, and SQL, allowing data professionals to use their preferred language for data analysis, machine learning, and data engineering tasks.

Yes, Databricks can scale to fit the needs of businesses of any size, from startups to large enterprises. Its flexible pricing and scalable infrastructure make it accessible for small teams, while its extensive capabilities support the complex requirements of larger organizations.