Data Developer Senior

Jobgether; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Health insurance

Meal and food allowances

Childcare assistance

Extended parental leave

Wellness programs access

Profit-sharing program

Job summary

Jobgether is seeking an experienced data engineer to lead engineering efforts across data platforms and pipelines. You will guide data engineers and analysts, shape the Bronze-Silver-Gold data layers, and ensure scalable, governed data solutions aligned with business needs.

You will design analytics models, implement Delta Lake structures, and deliver KPI reporting for financial datasets, collaborating with product and operations teams.

Qualifications

• Extensive data engineering experience with leadership or mentoring.
• Experience with financial KPIs and banking indicators.
• Advanced SQL, Python, PySpark for building and reviewing solutions.
• Hands-on Azure Databricks with Spark, PySpark and Spark SQL.
• Practical experience using Azure Data Factory to orchestrate pipelines.
• Knowledge of Medallion architecture (Bronze/Silver/Gold) and Delta Lake.
• Ability to model star schemas, OBTs, and fact/dimension tables.
• Data governance, quality management, and traceability expertise.
• Clear communication of complex concepts to technical and non-technical audiences.
• Experience prioritizing backlogs within agile teams.

Responsibilities

• Provide technical guidance to engineers and analysts, promoting best practices.
• Design and evolve data platform architecture and data layers.
• Ensure scalable, maintainable data architecture aligned with requirements.
• Refine technical demands with clear backlog items and acceptance criteria.
• Establish data quality practices with validation and documentation.
• Develop data solutions using SQL, Python, PySpark, and Azure tech.
• Build and orchestrate pipelines with Azure Databricks, ADF, Spark, Delta Lake.
• Maintain analytical data models such as star schemas and OBT structures.
• Deliver reliable finance KPIs and analytics reporting.
• Act as technical point of contact for stakeholders and translate goals into data solutions.
• Collaborate cross-functionally to improve governance and platform performance.

Skills

SQL

Python

PySpark

Data modeling

Stakeholder communication

Cloud data platforms

Data governance

Tools

Azure Databricks

Azure Data Factory

Delta Lake

Power BI

Job description

Accountabilities

- Provide technical guidance to data engineers and analysts, promoting engineering best practices, conducting technical reviews, and supporting the continuous improvement of data solutions.

- Design, structure, and evolve the data platform architecture, defining standards for data modeling, pipelines, governance, and Bronze, Silver, and Gold data layers.

- Ensure data architecture remains consistent, scalable, maintainable, and aligned with business and analytical requirements.

- Support the prioritization and refinement of technical demands, ensuring backlog items and user stories have clear requirements, acceptance criteria, and feasible implementation plans.

- Establish and maintain data quality practices, including validation, documentation, and traceability throughout the data lifecycle.

- Develop and review data engineering solutions using advanced SQL, Python, PySpark, and Azure-based technologies.

- Build, optimize, and orchestrate data pipelines using Azure Databricks, Azure Data Factory, Spark, Spark SQL, and Delta Lake.

- Design and maintain analytical data models, including star schemas, one big table (OBT) structures, and fact and dimension models.

- Support the reliable delivery of financial-sector KPIs and indicators, ensuring consistency and accuracy in reporting and analytics.

- Act as a technical point of contact for stakeholders across product, operations, and management, translating strategic objectives into effective data solutions.

- Collaborate with multidisciplinary teams to improve data governance, technical processes, and the overall performance of the data platform.

Requirements

- Extensive experience in data engineering, including technical leadership or mentoring of data engineering teams.

- Experience working with financial-sector KPIs and banking-related indicators.

- Advanced SQL skills and strong proficiency in Python and PySpark for developing, reviewing, and optimizing data solutions.

- Hands-on experience with Azure Databricks , including Spark, PySpark, and Spark SQL.

- Practical experience using Azure Data Factory (ADF) to orchestrate and manage data pipelines.

- Strong knowledge of Medallion architecture, including Bronze, Silver, and Gold layers, and experience working with Delta Lake.

- Experience designing and implementing analytical data models, including star schemas, OBT structures, and fact and dimension tables.

- Knowledge of data governance, data quality management, pipeline documentation, and data traceability.

- Ability to communicate technical concepts clearly to both technical and non-technical stakeholders.

- Experience managing and prioritizing technical backlogs within agile teams.

- Strong analytical thinking, problem-solving skills, attention to detail, and the ability to balance architectural decisions with practical delivery needs.

- A collaborative mindset and the ability to guide teams while maintaining high standards of technical quality.

Nice to have:

- Experience with Azure Purview or other data catalog and lineage tools.

- Familiarity with data CI/CD practices and tools such as Azure DevOps.

- Knowledge of business intelligence platforms such as Power BI or Looker for validating KPIs and analytical outputs.

- Experience supporting growth marketing teams with customer acquisition, activation, and retention analytics.

- Knowledge of data streaming technologies, including Spark Structured Streaming or Azure Event Hubs.

- Relevant Azure or Databricks certifications, such as DP-203 or Databricks Certified Data Engineer.

- Experience delivering large-scale, business-critical data projects.

- Knowledge of generative AI applications in data engineering and analytics.

Benefits

- Comprehensive health and dental insurance.

- Meal and food allowances.

- Childcare assistance.

- Extended parental leave.

- Access to fitness and wellness programs through Wellhub and TotalPass.

- Profit-sharing program (PLR).

- Life insurance.

- Continuous learning opportunities through an internal learning platform.

- Partnerships with online learning platforms and professional development courses.

- Language-learning resources.

- Employee discount programs.

- Free online resources supporting physical health, mental well-being, and overall wellness.

- Courses focused on pregnancy, parenting, and responsible family care.

- An inclusive workplace with dedicated health, wellness, and inclusion teams.

- Remote work within Brazil, with office attendance required for professionals residing in the Campinas metropolitan area, according to the applicable attendance policy.

- Salary: Compensation details were not specified in the original job description.