Hand Talk

Data Engineer

• Define the team's technical priorities, translating the Head of Data Engineering's business vision into actionable technical requirements.
• Mentorship and Code Review: Raise the team's technical standard through rigorous code reviews and continuous mentorship of data engineers.
• Governance and Standards: Establish data quality standards, pipeline observability, and data lineage documentation.
• Infrastructure Decisions: Lead the Infrastructure as Code (Terraform) strategy, ensuring consistent development, staging, and production environments.
• Data Culture: Promote a data-driven culture and increase adoption of the data platform by eliminating manual processes and silos.

Lead Data Engineer

Customer Success Manager

• Manage customers within a specific MRR band:
• ● Take ownership of the customer after onboarding, monitor their journey, and execute renewals;
• ● Build and maintain customer relationships, plan and execute initiatives focused on account expansion so the customer achieves and recognizes the expected results;
• ● Track achievement of objectives and keep expectations aligned according to the success criteria defined with the customer during onboarding;
• ● Analyze behavioral data and execute playbooks that directly impact customer health;
• ● Communicate results and improvement proposals to customers (email, phone calls, video calls, meetings, in-person visits, etc.);
• ● Identify and receive customer requests, recording opportunities and risks following internal communication processes between commercial and technical teams;
• ● Keep customer data and relationship records up to date in the platforms used in the CS process;
• ● Map upsell and cross-sell opportunities within your portfolio;
• ● Prepare and negotiate renewal, upsell and/or cross-sell proposals;
• ● Ensure coverage of your account base.
• Collaborate in building, improving, and automating the customer journey and CS and customer service processes:
• ● Contribute to creating action plans to increase retention and expansion and reduce churn;
• ● Propose solutions to issues and improvements in CS processes;
• ● Contribute to creating educational and guidance articles for customers;
• ● Help define CS metrics/indicators and monitor them for continuous improvement (NPS, Retention, Upsell, Health Score, etc.);
• ● Share behaviors, feedback, results and proposed actions/improvements with customers and internal teams, occasionally acting as a mediator;
• ● Think critically; be able to identify problems of different natures and apply durable, low-maintenance solutions that won't cause future issues, even if it requires learning and quickly applying new tools.
• Occasionally participate in events/livestreams as a moderator, and in interdisciplinary projects and initiatives.
• Occasionally make on-site client visits (if/when requested).

Senior Customer Success Analyst

• Design and lead the construction of a data platform on AWS that meets the needs of internal stakeholders;
• Identify and implement optimal architectural solutions to improve our data platform;
• Ensure data observability, quality, and reliability for internal and external stakeholders;
• Automate testing processes for pipelines to onboard new data sources, integrating tests into the CI/CD pipeline;
• Execute the data platform adoption plan in partnership with the data governance team;
• Collaborate with data scientists and software engineers to enable an integrated data workflow.

Senior Data Engineer

• Work closely with Full Stack developers, other data engineers, and data scientists to deliver production-ready data via APIs or ML models.
• Implement the architecture of a data platform (Data Lakehouse) and maintain the data lifecycle, ensuring integrity at every stage of the pipeline.
• Configure and optimize Apache Iceberg tables to support ACID transactions, schema evolution, and hidden partitioning, ensuring consistency and concurrent reads.
• Build automated Airflow workflows with preprocessing and metadata extraction using a factory pattern.
• Implement data contracts and automated pipeline quality tests, ensuring the Lakehouse is a reliable source of truth for the company's products.
• Apply DevOps best practices to ensure the platform infrastructure is provisioned with Terraform.

Mid-level Data Engineer

• Work with team members and architects from other teams to ensure a cohesive Data Platform architecture.
• Ensure the Data Platform (Data Lakehouse) architecture is scalable and robust.
• Design the taxonomy and structure of the Data Catalog so data is easily discoverable and accessible.
• Establish standards (file formats, compression, partitioning strategies) to maximize query efficiency.
• Lead the selection of new tools, architectural changes, and features, communicating trade-offs between cost, complexity, and performance to the team.
• Design security architecture using AWS Lake Formation to ensure data isolation and auditing.
• Implement and manage the data catalog with governance and access controls.

Senior Data Architect

Machine Learning Engineer

• Design, develop, and maintain robust backend applications and services to serve ML inference (FastAPI, Flask, or Node.js);
• Build and optimize pipelines for real-time or batch inference processing;
• Monitor and optimize model performance in production, ensuring low latency and high availability;
• Design distributed systems capable of supporting intensive Machine Learning workloads;
• Work closely with data scientists and ML engineers to translate research models into production-ready services;
• Identify and integrate emerging technologies to improve system performance and the end-user experience.

Lead Data Engineer

Senior Customer Success Analyst

Senior Data Engineer

Mid-level Data Engineer

Senior Data Architect

Senior Full Stack MLOps