We are looking for an experienced and highly skilled Senior Data Engineer to lead the design and development of our data infrastructure and pipelines. As a key member of the Data & Analytics team, you will play a pivotal role in scaling our data ecosystem, driving data engineering best practices, and mentoring junior engineers. This role is ideal for someone who thrives on solving complex data challenges and building systems that power business intelligence, analytics, and advanced data products. Key Responsibilities: Design and build robust, scalable, and secure data pipelines and models. Lead the complete lifecycle of ETL/ELT processes, encompassing data intake, transformation, and storage including the concept of SCD type2. Collaborate with data scientists, analysts, backend and product teams to define data requirements and deliver impactful data solutions. Maintain and oversee the data infrastructure, including cloud storage, processing frameworks, and orchestration tools. Build logical and physical data model using any data modeling tool Champion data governance practices, focusing on data quality, lineage tracking, and catalog management. Guarantee adherence of data systems to privacy regulations and organizational guidelines. Guide junior engineers, conduct code reviews, and foster knowledge sharing and technical best practices within the team. Required Skills & Qualifications: Bachelor's or Master's degree in Computer Science, Engineering, or a related discipline. Minimum of 5 years of practical experience in a data engineering or comparable position. Demonstrated expertise in SQL and Python (or similar languages such as Scala/Java). Extensive experience with data pipeline orchestration tools (e.g., Airflow, dbt, ). Proficiency in cloud data platforms, including AWS (Redshift, S3, Glue), or GCP (BigQuery, Dataflow), or Azure (Data Factory, Synapse). Familiarity with big data technologies (e.g., Spark, Kafka, Hive) and other data tools. Solid grasp of data warehousing principles, data modeling techniques, and performance tuning. (e.g. Erwin Data Modeler, MySQL Workbench) Exceptional problem-solving abilities coupled with a proactive and team-oriented approach.
We are seeking a strategic and innovative Senior Data Scientist to join our high-performing Data Science team. In this role, you will lead the design, development, and deployment of advanced analytics and machine learning solutions that directly impact business outcomes. You will collaborate cross-functionally with product, engineering, and business teams to translate complex data into actionable insights and data products. Key Responsibilities Lead and execute end-to-end data science projects, encompassing problem definition, data exploration, model creation, assessment, and deployment. Develop and deploy predictive models, optimization techniques, and statistical analyses to address tangible business needs. Articulate complex findings through clear and persuasive storytelling for both technical experts and non-technical stakeholders. Spearhead experimentation methodologies, such as A/B testing, to enhance product features and overall business outcomes. Partner with data engineering teams to establish dependable and scalable data infrastructure and production-ready models. Guide and mentor junior data scientists, while also fostering team best practices and contributing to research endeavors. Required Qualifications & Skills: Masters or PhD in Computer Science, Statistics, Mathematics, or a related discipline. 5+ years of practical experience in data science, including deploying models to production. Expertise in Python and SQL; Solid background in ML frameworks such as scikit-learn, TensorFlow, PyTorch, and XGBoost. Competence in data visualization tools like Tableau, Power BI, matplotlib, and Plotly. Comprehensive knowledge of statistics, machine learning principles, and experimental design. Experience with cloud platforms (AWS, GCP, or Azure) and Git for version control. Exposure to MLOps tools and methodologies (e.g., MLflow, Kubeflow, Docker, CI/CD). Familiarity with NLP, time series forecasting, or recommendation systems is a plus. Knowledge of big data technologies (Spark, Hive, Presto) is desirable.
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