Salla E-Commerce Platform

QA Engineer

• Data Pipeline Validation: Test the integrity of data ingestion and transformation processes to ensure models are trained on high-quality datasets.
• API & Model Testing: Conduct deep API testing using tools like APIDog to validate model responses, handling complex edge cases such as zero-value parameters or unauthorized access.
• Generative Model Output Accuracy: Define and implement evaluation frameworks to measure the quality of generative AI outputs — including relevance, factual accuracy, coherence, and hallucination rate — using techniques such as LLM-as-a-judge, human-in-the-loop scoring, and automated benchmark suites.
• AI Agent Response Validation: Design test strategies to assess the correctness and reliability of agentic AI responses, covering tool-call accuracy, multi-step reasoning integrity, goal completion rate, and graceful failure handling for out-of-scope or adversarial inputs.
• Security Testing: Proactively identify vulnerabilities like request interception and price manipulation to safeguard the system including prompt injection and jailbreak attempts specific to generative AI surfaces.
• UI Automation: Build and maintain scalable automation suites using low-code tools like mabl, aiming for high Mabl Test Coverage %.
• AI Tooling Advocacy: Champion the use of AI assistants like Jira Rovo for requirement analysis and test case generation to increase team velocity.
• Root Cause Analysis (RCA): Lead the RCA process for production bugs and developer-rejected tickets to continuously improve the "Definition of Done".

Senior Quality Engineer II – AI, Data

Data Engineer

• Pipeline Engineering: Design, build, and maintain scalable ETL/ELT pipelines from diverse sources including Data Lakes, Production ClickHouse instances, flat files, and various APIs.
• Infrastructure & Orchestration: Configure and optimize our Data Warehouse infrastructure (ClickHouse) and orchestration layers (Mage.ai).
• Engineering Excellence: Implement and manage "engineering-grade" CI/CD workflows, conduct rigorous PR reviews, and ensure robust dependency management across the stack.
• Data Modeling & Architecture: Implement Medallion architecture (Bronze/Silver/Gold) and maintain high-performance data models using dbt.
• Quality & Observability: Build automated data quality monitoring and alerting; proactively escalate upstream data issues to engineering teams and keep stakeholders informed of pipeline health.
• Advanced Data Flows: Develop reverse ETL (rETL) pipelines and expose secure data APIs to enable seamless data consumption across the organization.
• Strategic Integration: Manage event streaming and real-time data ingestion (Kafka, CDC) to support high-volume product analytics and tracking.

Senior Data Engineer – Data Platform

Data Analyst

• Identify and acquire data from different data sources (production, warehouse, external sources) and organize the data in usable formats.
• Develop and implement data collection systems, pipelines, data analytics and other strategies that optimize statistical efficiency and quality.
• Interpret data, analyze results using statistical techniques, create visualizations of data and provide reports, and dashboards.
• Identify, analyze, and interpret trends or patterns in complex data sets.
• Use BI tools to analyze data, and extract useful business insights.
• Filter and “clean” data by reviewing reports, dashboards, and performance indicators to locate and correct problems.
• Locate and define new process improvement opportunities.
• Supporting business departments and responding to their data-related requests and keeping track of these requests.
• Supporting data engineers in identifying and revising reporting requirements.
• Supporting initiatives for data integrity and normalization.
• Training end-users on new reports and dashboards.
• Work with management to prioritize business and information needs.

Senior Data Analyst

• Collect, clean, and organize data from production, warehouse, and external sources to ensure accuracy and reliability.
• Develop and maintain data collection systems, pipelines, and processes that optimize efficiency and quality.
• Perform exploratory data analysis (EDA) to identify patterns, trends, and opportunities in complex datasets.
• Use BI tools to build and maintain dashboards, reports, and visualizations that support business needs and decision-making.
• Interpret data and provide actionable insights to stakeholders through reports, dashboards, and presentations.
• Monitor data quality, validate results, and support initiatives for data integrity and normalization.
• Document data sources, processes, and definitions to improve knowledge-sharing and consistency across the team.
• Collaborate with cross-functional stakeholders to gather requirements and deliver data solutions aligned with business priorities.
• Support testing, troubleshooting, and improvements of reporting systems and pipelines.
• Train end-users on new reports, dashboards, and data tools to enhance adoption and effectiveness.
• Work with senior team members and management to define metrics, track performance, and prioritize business and information needs.
• Identify and recommend process improvements and opportunities for better data-driven decision-making.

Engineering Manager

• Identify, scope, and prioritize AI product opportunities that directly impact merchant revenue, retention, or operational efficiency
• Develop a rolling roadmap of AI-powered experiences validated against merchant needs, market dynamics, and business impact
• Define success metrics for each initiative and kill or scale projects based on data, not momentum
• Ship your first product within the first quarter — we value velocity and learning over perfection
• Build on top of existing AI infrastructure across the department — your job is to create products, not rebuild engines
• Establish a rapid prototyping and validation cadence: concept → prototype → merchant pilot → scale or kill
• Build and develop a versatile team that blends data science, engineering, and product thinking
• Create a team culture of experimentation, merchant obsession, and fast iteration
• Partner across the AI department to leverage generative AI capabilities, recommendation signals, and platform infrastructure for new product surfaces
• Work closely with product, design, and business stakeholders to ensure AI experiences translate into merchant outcomes
• Coordinate with ML platform and operations teams for infrastructure needs without building parallel systems

AI/ML Engineering Manager

Product Designer

• Support product discovery by contributing to user understanding, problem framing, and early design exploration
• Translate product requirements into design concepts, user flows, wireframes, and UI components using tools such as Figma
• Collaborate with Product Managers to align on problem scope, constraints, and design intent
• Work closely with engineering to ensure designs are clear, feasible, and implementation-ready
• Participate in design reviews, usability feedback sessions, and iteration cycles
• Support testing, release readiness, and post-launch design refinements
• Maintain clear design documentation and collaborate through tools such as Jira or similar product workflow systems

Senior Quality Engineer II – AI, Data

Senior Data Engineer – Data Platform

Senior Data Analyst

Data Analyst

AI/ML Engineering Manager

Associate Product Designer – Tamheer