• You will develop and maintain data models and structures that enable efficient querying and analysis; • You will design, develop, and maintain data pipelines to transform and process large volumes of data from various sources, while adding business context and semantics to the data; • You will implement automated data quality checks to guarantee data quality and consistency across the whole data life cycle. • Live the mission: inspire and empower others by genuinely caring for your own wellbeing and your colleagues. Bring wellbeing to the forefront of work, and create a supportive environment where everyone feels comfortable taking care of themselves, taking time off, and finding work-life wellness.

Role Description Our client is seeking a Senior Cloud Data Engineer to join their Engineering team. In this role, you will be a key technical contributor responsible for building, optimizing, and maintaining high-performance data processing engines. You will apply your deep expertise in Spark Best Practices to develop modular, portable PySpark applications, ensuring their platform remains provider-agnostic while operating within modern environments like Microsoft Fabric. As an experienced member of the Data Engineering team, you will focus on: - Refactoring legacy logic into scalable Python-centric solutions - Optimizing their data lakehouse architecture - Contributing to the overall technical excellence of our client's data platform Qualifications - Bachelor’s degree in computer science, Information Systems, Engineering, or a closely related field - Senior-Level Spark: 6+ years of experience with Spark/PySpark, with the ability to diagnose performance bottlenecks via the Spark UI and optimize complex DAGs - Advanced Python: Strong proficiency in production-grade Python, with experience building reusable libraries and implementing automated testing - SQL Proficiency: Solid T-SQL skills to accurately interpret and migrate existing logic into the new Python-centric environment - Azure Synapse Analytics & Dedicated SQL Pools: Solid experience working within Azure Synapse, specifically with Dedicated SQL Pools and Data Factory working with complex pipelines - Data Lakehouse Expertise: Hands-on experience with Delta Lake and Parquet, specifically in high-volume environments - Agnostic Mindset: Experience working with open-source standards and containerization (Docker) to ensure data workloads can be ported across different cloud infrastructures - Collaborative Engineering: Proven track record of working within a team to successfully deliver and modernize large-scale data workloads - Ensure high levels of security, compliance, and data governance across data engineering processes Company Description

Role Description We are seeking a Senior Analytics Engineer who combines strong data engineering fundamentals with advanced analytical thinking and business acumen. This role is not just about building pipelines; it’s about designing the analytical foundation that powers product decisions, experimentation, and machine learning initiatives. You will translate complex restaurant operational and customer behavior data into scalable, reliable, and insight-ready data models that drive measurable business impact. You will act as a bridge between engineering, analytics, product, and data science ensuring our data platform supports both robust technical systems and high-quality decision-making. What You’ll Own - Business Analytics & Decision Enablement - Translate ambiguous business questions into structured analytical frameworks - Develop KPI definitions, metric layers, and standardized reporting logic across teams - Partner with Product and Growth teams to define success metrics and measurement strategies - Deliver deep-dive analyses on ordering behavior, churn risk, marketplace performance, and operational efficiency - Present insights and trade-offs clearly to senior stakeholders and leadership - Experimentation & Advanced Analytics - Own and scale A/B testing infrastructure, including experiment tracking and evaluation pipelines - Define best practices for hypothesis testing, power analysis, sequential testing, and causal inference - Build experimentation dashboards and automated reporting systems - Partner with Data Science to productionize predictive models and design reusable ML-ready datasets - Support feature engineering and monitoring for production ML systems - Analytics Engineering & Strategic Technical Leadership - Design and maintain scalable ELT/ETL workflows that power analytics, experimentation, and ML use cases - Build and optimize dimensional data models (star schemas, data vault, etc.) to enable self-service analytics - Create trusted, well-documented datasets for product, finance, operations, and growth teams - Improve data quality, observability, lineage, and governance across the analytics ecosystem - Optimize performance across warehouse and transformation layers - Drive best practices in analytics engineering across the organization - Influence tooling decisions (Airflow, dbt, Snowflake, Spark, etc.) with scalability and business usability in mind - Mentor analytics engineers and elevate modeling and measurement standards - Champion a data-driven culture grounded in experimentation and measurable impact Qualifications - 7+ years of experience in analytics engineering, data engineering, or advanced analytics roles - Deep expertise in SQL and modern data modeling techniques - Strong proficiency in Python (or similar) for data transformation, statistical analysis, and ML integration - Experience working with modern data stack tools (e.g., Airflow, dbt, Snowflake, BigQuery, Redshift, Spark) - Proven experience building scalable data warehouse environments Requirements - Strong understanding of statistics, hypothesis testing, and causal inference - Experience designing and operating A/B testing frameworks - Ability to define metrics, KPIs, and experimentation standards across teams - Experience supporting production ML workflows or predictive modeling initiatives - Strong systems thinking with the ability to connect technical decisions to business outcomes Preferred Experience - Experience in restaurant technology, POS systems, or digital ordering platforms - Exposure to customer lifecycle analytics, marketplace analytics, or growth experimentation - Familiarity with real-time data pipelines (e.g., Kafka or similar) - Experience mentoring engineers or leading cross-functional initiatives Why This Role is Unique - You’ll shape the analytical backbone of a fast-scaling product ecosystem - Your work will directly influence revenue, product strategy, and customer experience - You’ll operate at the intersection of engineering rigor and business decision-making - You’ll help build experimentation and ML systems that power millions of restaurant transactions daily

• Design and implement scalable, secure data pipelines across multi-cloud ecosystems (Azure, AWS, GCP). • Develop and maintain ETL/ELT workflows using modern orchestration tools like Apache Airflow, Spark, and cloud-native services. • Build and optimize data processing frameworks with technologies such as Apache Beam, DLT, DBT, and Snowflake. • Collaborate with data scientists and AI engineers to operationalize machine learning models and integrate AI services (Azure AI, AWS SageMaker, GCP Vertex AI, Databricks MLflow). • Implement data quality, governance, and observability practices to ensure accuracy, consistency, and compliance. • Design and maintain data models and analytical layers that power self-service analytics and BI solutions. • Continuously evaluate emerging tools and frameworks to deliver best-in-class performance and cost efficiency.