Senior Data Developer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Developer based in Brazil.

This role focuses on modernizing complex data workloads from legacy environments to a modern cloud-based data platform.

You will analyze, convert, implement, and validate ETL pipelines, SQL processes, integrations, and other critical data workloads.

The position combines hands-on data engineering with migration, automation, testing, reconciliation, and production-quality validation.

You will also use coding agents and AI-assisted development tools to accelerate engineering activities while maintaining strong technical judgment.

The role offers the opportunity to contribute to migration tooling, reusable automation, quality gates, and scalable engineering practices.

You will collaborate closely with Data Engineers, Tech Leads, architects, client specialists, and cloud platform experts.

This is a technically demanding environment suited to professionals who enjoy solving ambiguous legacy-system challenges and turning them into reliable modern data solutions.

Accountabilities:

• Analyze legacy workloads including SSIS packages, stored procedures, views, functions, SQL Agent Jobs, PySpark/Sqoop pipelines, and integration scripts.
• Identify tables, files, connections, parameters, dependencies, business rules, and potential side effects associated with each workload.
• Investigate undocumented or implicit business rules and behaviors within legacy processes.
• Support workload classification across migration strategies such as migrate, convert, refactor, replace, eliminate, or postpone.
• Document technical risks, dependencies, limitations, open questions, and relevant findings from legacy assessments.
• Contribute to technical inventories and dependency mapping across the legacy environment.
• Implement T-SQL conversions to Databricks SQL, Spark SQL, or PySpark.
• Convert or rebuild SSIS workflows using notebooks, jobs, pipelines, and workflows on the target platform.
• Modernize SQL Agent Jobs and other scheduling mechanisms according to defined orchestration standards.
• Apply established architecture and development patterns to migrated workloads.
• Identify limitations or opportunities for simplification and propose improvements to migration standards.
• Address platform differences involving data types, NULL behavior, decimal precision, dates, time zones, collation, sorting, functions, and transactional behavior.
• Preserve business rules and expected outcomes without unnecessarily reproducing limitations or complexity from legacy solutions.
• Participate in code reviews and support squad members with technical investigations.
• Use coding agents such as Claude Code, Codex, or equivalent tools for code analysis, implementation, refactoring, testing, troubleshooting, and documentation.
• Provide AI coding tools with appropriate technical context, requirements, architectural patterns, examples, and acceptance criteria.
• Critically review AI-generated code before acceptance or promotion to ensure correctness, security, performance, maintainability, and semantic equivalence.
• Apply iterative cycles of generation, execution, error analysis, correction, and validation.
• Transform recurring corrections and lessons learned into reusable instructions, examples, rules, tests, or skills for the squad.
• Contribute to the migration harness by improving context, instructions, tools, scripts, validators, and automation.
• Integrate AI-assisted engineering activities with Git, pull requests, CI/CD, and established review processes.
• Improve the accessibility of logs, errors, tests, metadata, and documentation for development agents.
• Create automated execution and feedback cycles that allow conversions to be tested and verified repeatedly.
• Identify activities suitable for automation and distinguish them from situations requiring human analysis or approval.
• Create unit, integration, and regression tests for converted pipelines and components.
• Execute parity validations between legacy and modernized workloads.
• Compare schemas, row counts, checksums, aggregations, business rules, and detailed results to identify discrepancies.
• Investigate whether differences originate from code, data, environment, or interpretation of business rules.
• Create test data and scenarios covering both common and edge cases.
• Contribute to golden datasets, golden queries, and other artifacts used to evaluate conversions.
• Produce and maintain validation evidence for migrated workloads.
• Use Lakebridge and other approved tools for workload analysis, conversion, and reconciliation.
• Develop Python utilities and automations to reduce manual migration activities.
• Support integration between assessment, metadata, lineage, conversion, and validation tools.
• Investigate complex integrations involving linked servers, Oracle connections, SAP, file shares, email services, and other external dependencies.
• Collaborate with architecture, infrastructure, and security teams to resolve network, access, and authentication dependencies.
• Support coexistence testing, cutover, rollback, and legacy-component decommissioning activities.
• Follow enterprise security and governance requirements when using generative AI tools.
• Protect credentials, secrets, proprietary code, personal data, and other sensitive information.
• Maintain traceability between legacy artifacts, converted code, changes, and validation evidence.
• Collaborate with Data Engineers, Tech Leads, architects, client specialists, and platform experts.
• Participate in technical discussions, refinement sessions, code reviews, and troubleshooting activities.
• Communicate risks, blockers, dependencies, and technical alternatives clearly.
• Maintain technical documentation covering mappings, decisions, runbooks, test evidence, and operational guidance.
• Share lessons learned and contribute to the evolution of migration practices and tooling.

Requirements:

• Solid professional experience as a Data Engineer, Data Developer, or in a comparable senior data engineering role.
• Strong hands-on experience developing and maintaining data pipelines.
• Advanced SQL skills and the ability to understand and troubleshoot complex queries and data processes.
• Practical experience with SQL Server, including T-SQL, stored procedures, views, functions, SQL Agent Jobs, and cross-database integrations.
• Hands-on experience with SSIS, including reading, developing, or troubleshooting .dtsx packages, control flows, data flows, parameters, and connections.
• Experience with Databricks or Apache Spark-based data platforms.
• Knowledge of Spark SQL, PySpark, notebooks, Delta Lake, and job execution.
• Experience with Python for pipeline development, scripting, automation, or testing.
• Familiarity with Azure services such as ADLS Gen2, Azure SQL, Key Vault, identities, and networking.
• Experience with Git, pull requests, code reviews, and CI/CD practices.
• Experience with automated testing and validation of data pipelines.
• Previous experience in data migration, modernization, or legacy-environment support projects.
• Ability to investigate poorly documented systems and work effectively with ambiguity.
• Practical experience using coding agents such as Claude Code, Codex, or equivalent tools in real development activities.
• Ability to independently review and validate AI-generated code and make sound technical decisions.
• Familiarity with providing context, instructions, and executable feedback to coding agents.
• Knowledge of orchestration technologies such as Apache Airflow, Databricks Workflows, or equivalent solutions.
• Strong analytical and troubleshooting skills across code, data, logs, and infrastructure.
• Strong communication skills and ability to collaborate with multiple technical teams.
• Fluency in Portuguese and sufficient English proficiency for technical documentation and interaction.
• Experience in regulated industries such as healthcare or financial services is an advantage.
• Knowledge of LGPD and practices for protecting sensitive data is a plus.
• Familiarity with metadata management, data lineage, and data governance is beneficial.
• Knowledge of Terraform, Databricks Asset Bundles, or other Infrastructure as Code practices is a plus.

Benefits:

• Remote work model, with an office presence requirement for professionals residing in the Campinas metropolitan area according to the applicable policy.
• Health and dental insurance.
• Meal and food allowance.
• Childcare assistance.
• Extended parental leave.
• Wellhub and TotalPass partnerships for fitness and wellness.
• Profit Sharing and Results (PLR) program.
• Life insurance.
• Continuous learning platform and professional development resources.
• Access to online course platforms and language-learning resources.
• Discount club and partner benefits.
• Free online platform focused on physical health, mental health, and well-being.
• Support for pregnancy and responsible parenthood through dedicated programs.
• Dedicated Health & Well-being team and inclusion specialists.
• Affinity groups and initiatives supporting diversity and inclusion.
• Opportunities to work with modern data, cloud, automation, and AI-assisted engineering technologies.
• Collaborative environment focused on innovation, technical excellence, knowledge sharing, and continuous development.