Job Summary: The Senior Data Engineer serves as technical leadership, supporting the Tech Lead, developing the team, and disseminating knowledge, with end-to-end autonomy in technical decisions and strategic vision. Key Highlights: 1. Technical and strategic leadership in complex data projects 2. Autonomy in technical decision-making and mentoring the team 3. Focus on innovation, cost optimization, and scalable architectures **Role in the Company:** The Senior Data Engineer at Dataside acts as technical leadership on projects, supporting the Tech Lead and contributing to team development and knowledge dissemination. Possesses autonomy in end-to-end technical decisions, ensuring deliverables align with client needs. Additionally, conducts mentoring, rigorous code reviews, and drives the company’s technical evolution. With a strategic view of data value, connects deliverables to business impact. More than building solutions, acts as a consultative partner—provoking reflection and strengthening client relationships through well-founded proposals and trust. **Responsibilities:** Serve as lead engineer on contracts and autonomously drive technical decisions. Support the Tech Lead in technical and strategic project leadership. Conduct in-depth technical reviews and mentoring with the team. Propose batch and streaming architectures with a focus on scalability, efficiency, and business alignment. Build performance improvement and cost reduction plans. Diagnose technical and operational bottlenecks and propose sustainable solutions. Communicate solutions clearly across different audiences (business, technical, management). Work with multiple integrations, high-volume data, and performance-critical requirements. **Requirements:** Proven experience in highly complex data engineering projects. Ability to operate effectively in environments unfamiliar with the technology stack, learning quickly. Strong advanced Spark and Python expertise, with solid software engineering practices (SOLID principles). Advanced SQL, including query tuning and execution plan analysis. Experience integrating via RESTful APIs and distributed systems. Proficiency in AWS analytics stack, including: S3, Athena, Redshift, Glue, EMR, Kinesis, Lambda, Step Functions, Lake Formation, Secrets Manager, CloudWatch, SNS, Cost Explorer, Glue DataBrew, Amazon Macie. Hands-on experience with Databricks, including: cluster and policy management, Unity Catalog (lineage, access control, versioning), SQL Warehouse, Workflows, Delta Live Tables (DLT). Knowledge of diverse data modeling techniques: Data Vault, dimensional modeling, event-driven pipelines. Familiarity with NoSQL databases such as DynamoDB, MongoDB, and Cassandra. Expertise in cluster sizing, distributed computing, and detailed cost analysis. **Hard Skills:** Advanced Spark, including Catalyst Optimizer, persistence, broadcast joins, caching, and bucketing. Python following SOLID principles and component reuse. Advanced SQL with tuning, partitioning, and execution plan optimization. Consumption and integration via RESTful APIs. Advanced Databricks: Unity Catalog, SQL Warehouse, Workflows, DLT, Pools, and cluster configuration. AWS Analytics Stack: S3, Athena, Glue, EMR, Redshift, Kinesis, Lambda, Step Functions, Lake Formation, Glue DataBrew, Macie, CloudWatch, SNS, Cost Explorer. Advanced Amazon Redshift: optimization using sort keys, compression, vacuum tuning, advanced WLM, SVL/SVV logs, Redshift Spectrum, hybrid architecture with S3. Data modeling: Data Vault, 3NF, dimensional, event-driven pipelines. NoSQL databases (DynamoDB, MongoDB). CI/CD with Git, GitHub Actions, or GitLab CI. Unit testing concepts applied to data pipelines. Observability and data quality frameworks and practices: Great Expectations, Airflow, Prometheus, Grafana. Development of reusable and composable solutions, focused on Data Products and Data Mesh strategies. Cloud cost evaluation and planning: use of Cost Explorer, sizing with AWS Pricing Calculator. **Soft Skills:** Technical leadership and strategic influence. Ability to “learn how to learn.” Clarity and depth in communication with both technical and business stakeholders. Consultative mindset, active listening, and proactivity. Collaborative spirit, mentoring, and team development. Organization, delivery focus, and strong sense of responsibility. Intermediate English. **Desired Certifications:** AWS Data Engineer Associate. AWS Solutions Architect Associate. **Differentiators:** AWS Certified Data Analytics Specialty. AWS Solutions Architect – Professional. Databricks Certified Professional Data Engineer. * **Our Benefits:** * **Health Insurance Allowance**: monthly financial support to help cover your private health insurance plan. * **Wellhub**, to keep your body and mind active—in the way you prefer. * **Online Therapy**, fully covered by the company, because mental health matters. * **Online Nutrition Consultations**, up to two sessions per month to support your dietary health. * **Life Insurance**, with a policy value of R$125,000, providing greater security for you and your family. * **Birthday Day Off**, because your day deserves to be special. * **Paid Time Off**, so you can recharge your energy. * **Internal Gamification**, turning achievements into rewards and recognition. * **Educational Partnerships** **with universities**, such as FIAP, Anhanguera, and Instituto Infnet, to support your growth and learning. * **Technical Certification Bonus**, recognizing and rewarding your effort in learning. ***We value every voice and every person, because we know diversity makes us more innovative and stronger.***