Jobs at SunnyData

Designs, builds, deploys, and operationalizes scalable data engineering solutions for customers. Responsibilities include developing data pipelines, transformations, data quality and governance solutions, analyzing data sources, testing data movement, resolving defects and incidents, and supporting AI, machine learning, and business intelligence initiatives. The role also involves technical customer guidance, cross-account progress tracking, collaboration, and end-user education.

Lead customer engagements and pre-sales for Databricks-based data platforms. Architect and deliver end-to-end data engineering solutions supporting BI, ML/AI, and analytics; manage multiple accounts, mentor teams, ensure data quality, governance, and production readiness, and produce technical documentation and presentations.

Design and deliver Databricks-centric data architectures, lead pre-sales and billable engagements, build end-to-end data engineering pipelines for AI/ML and BI, mentor teams, ensure data governance/security/quality, and resolve data system issues while producing solution documentation and presentations.

Coordinate technical project logistics, meetings, agile ceremonies, action items, dependencies, deadlines, stakeholder communication, and risk resolution. Facilitate collaboration among engineers, technical leads, clients, and business stakeholders while promoting lightweight agile practices. The role supports data and software projects, requiring strong organization, communication, problem-solving, technical acumen, and stakeholder management skills.

Coordinates technical project execution by organizing meetings and agile ceremonies, tracking action items, dependencies, risks, and deadlines, and facilitating communication among technical teams and clients. Supports lightweight agile practices, stakeholder alignment, problem-solving, and delivery momentum across data, software, analytics, ML, and AI projects.

Coordinate technical project meetings, agile ceremonies, logistics, action items, dependencies, risks, and deadlines. Facilitate communication among technical teams, clients, and stakeholders while supporting lightweight agile practices and delivery momentum. The role requires strong stakeholder management, organization, analytical problem-solving, communication, and technical acumen, with experience supporting data or software projects in agile environments.

Designs, builds, deploys, and operationalizes scalable data engineering solutions using Databricks. Responsibilities include data transformation, migration, quality, governance, security, performance optimization, testing, incident resolution, and analytics enablement. The role advises customers, manages multiple accounts and projects, collaborates across teams, and educates users on data products and analytical environments. Requires strong SQL and PySpark skills, cloud experience, consulting experience, and leadership capabilities.

Designs, builds, deploys, and operationalizes complex data engineering solutions for multiple customers. Responsibilities include Databricks migrations, data pipeline development, transformations, optimization, governance, security, quality, testing, incident resolution, and data source analysis. The role also involves advising customers, tracking project progress, collaborating across teams, and educating users on data products and analytics environments.

Design, build, deploy, and operationalize scalable data engineering solutions using Databricks. Advise customers on technical decisions, migrations, data quality, governance, security, performance optimization, and analytics environments. Analyze data sources, develop transformations, test pipelines, resolve incidents, track customer projects, and collaborate with internal teams and end users to deliver data products supporting AI, machine learning, and business intelligence.

Designs and executes manual and automated tests for data-driven applications, APIs, databases, and data pipelines. Develops test plans, regression tests, and automation; validates data accuracy and integrity; tracks defects; collaborates with engineering, data teams, and stakeholders; documents testing standards; and uses AI tools to improve test coverage and efficiency.

The QA Engineer translates requirements into test plans and cases, executes regression and automated testing, tracks defects, and validates fixes. The role focuses on data quality across applications, APIs, databases, and pipelines, while collaborating with engineering, data teams, and stakeholders. Responsibilities include establishing testing standards, improving automation and coverage, documenting results, troubleshooting defects, and using AI tools to improve testing efficiency.

Develop and execute manual and automated tests for data-focused applications, APIs, databases, and pipelines. Translate requirements into test plans, validate data quality and integrity, track defects, collaborate with engineering and stakeholders, and improve testing processes, automation, coverage, and efficiency using AI tools.

Lead the design, development, and production deployment of machine learning models and predictive analytics pipelines. Collaborate with engineering, product, analytics, and data teams to optimize cloud-based data solutions, automate workflows, and apply statistical modeling to business challenges. Mentor junior data scientists, establish MLOps best practices, and communicate insights and recommendations to stakeholders and executives.

Leads the design, development, deployment, and operationalization of machine learning models and predictive analytics pipelines. Works with large datasets, data engineers, and cross-functional teams to build cloud-based solutions, automate workflows, and apply statistical modeling. Mentors junior data scientists, communicates recommendations to stakeholders, and contributes to MLOps and model-development best practices.

Lead the design, development, deployment, and operationalization of machine learning models and predictive analytics pipelines. Collaborate with engineering, product, analytics, and data engineering teams to build scalable cloud-based data solutions, automate workflows, and translate business challenges into data-driven recommendations. Mentor junior data scientists, establish best practices, and communicate findings to stakeholders and executives.

Collaborates with clients to define business needs, designs data and business architectures, identifies data and AI opportunities, and translates requirements into technical specifications. Leads workshops, gap analyses, stakeholder alignment, governance improvements, and solution implementation. Develops delivery frameworks and accelerators while supporting client relationship growth and quantifying business value and ROI.

Collaborates with clients to define business needs, design data and business architectures, identify AI and analytics opportunities, and translate strategy into technical requirements. Leads workshops, gap analyses, stakeholder alignment, and solution implementation while improving data governance and delivery practices. Develops reusable frameworks, quantifies business value, and supports client relationship expansion.

Consults with clients to define business needs, design data-driven architectures, identify AI and process-improvement opportunities, and translate strategy into technical requirements. Leads workshops, aligns stakeholders, supports implementation teams, develops delivery frameworks, and helps expand client relationships. The role requires strong SQL, visualization, data governance, analytics, client-facing consulting, stakeholder management, and business analysis expertise.

Design, build, deploy, and operationalize scalable data engineering solutions for customers. Responsibilities include developing pipelines, transforming and cleansing data, analyzing sources, implementing governance, security, and quality practices, testing data movement and transformation code, resolving defects and incidents, and advising customers on technical decisions. The role also supports AI, machine learning, business intelligence, and data product adoption while collaborating across teams and educating end users.

As a Solutions Architect, guide customer data architecture needs, design and implement data solutions on Databricks, manage multiple accounts, and collaborate with sales teams while ensuring data governance and quality.