About the Role Were looking for a hands-on Data Engineer who can take full ownership of the data engineering lifecycle-from data ingestion to deployment-in a hybrid cloud environment built on Microsoft Fabric and Google Cloud Platform (GCP). You'll be stepping into an existing ecosystem with mature pipelines, strong DevOps practices, and a close-knit cross-functional team. This role is ideal for someone who is comfortable working independently, can manage multiple priorities, and is equally adept at building new pipelines and improving existing ones Key Responsibilities Develop, enhance, and maintain end-to-end data pipelines across Microsoft Fabric and GCP. Work with tools like Fabric Dataflows, Synapse (Lakehouse), BigQuery, Composer, and others to support both batch and near real-time processing. Build and maintain ELT/ETL frameworks using Python, PySpark, SQL, and relevant platform-native services. Deploy pipelines and manage infra via Git-based CI/CD workflows (e.g., Azure DevOps / GitHub Actions). Collaborate with data analysts, product managers, and other engineers to translate business needs into scalable data models and flows. Monitor data quality, job performance, and proactively resolve data issues. Support and improve our data observability, governance, and documentation practices. Work independently and act as a point of contact for ongoing pipeline or platform-related discussions. Required Skills Strong experience with both Microsoft Fabric (Synapse, OneLake, Dataflows) and GCP (BigQuery, Cloud Storage, Composer). Solid hands-on with SQL, Python, and Spark/PySpark. Proven experience in building and deploying modular, reusable, and production-grade data pipelines. Experience in managing version-controlled codebases and CI/CD for data workflows. Good understanding of data warehousing principles, data modeling, and performance tuning. Familiar with monitoring tools, job orchestration, and alerting strategies. Nice to Have Prior work with Power BI, Looker, or any BI integration layer. Cloud certifications in GCP or Microsoft are a plus.
Job Title: AI Engineer / Data Scientist Sales & Marketing About the Role We are seeking a highly skilled AI Engineer / Data Scientist to join our team and be part of the innovation at the intersection of sales, marketing, and advanced intelligence and analytics . The ideal candidate will leverage machine learning, generative AI, and data science techniques to optimize customer acquisition, engagement, and retention strategies. This role requires both technical expertise in AI/ML modeling and a strong business mindset to translate data-driven insights into impactful sales and marketing outcomes. Key Responsibilities Design, develop, and deploy AI/ML models to support sales forecasting, lead scoring, customer segmentation, and personalization. Build and maintain predictive and prescriptive analytics pipelines for marketing campaigns and sales operations. Apply generative AI and NLP techniques (e.g., LLMs, chatbots, content generation) to enhance customer engagement and marketing automation. Collaborate with marketing and sales stakeholders to translate business challenges into data-driven solutions . Perform A/B testing, uplift modeling, and attribution analysis to optimize campaign performance. Integrate AI/ML models into production systems in partnership with data engineering and software development teams. Communicate findings and recommendations to non-technical stakeholders through clear visualizations and storytelling. Stay up to date with the latest advancements in AI, machine learning, and martech tools . Qualifications Required Bachelors or Masters degree in Computer Science, Data Science, Statistics, Mathematics, or a related field . Proven experience as a Data Scientist, Machine Learning Engineer, or AI Engineer . Strong proficiency in Python (pandas, scikit-learn, TensorFlow/PyTorch, NLP libraries) . Hands-on experience with SQL and data visualization tools (e.g., Tableau, Power BI, Looker). Background in sales/marketing analytics Experience deploying models in production (e.g., APIs, cloud platforms like AWS, GCP, or Azure). Excellent communication skills and ability to work cross-functionally with business and technical teams. Preferred Experience with CRM and marketing platforms (e.g., Salesforce, HubSpot). Knowledge of marketing attribution models, recommendation systems, and churn prediction . Familiarity with generative AI applications in marketing (e.g., content generation, customer service automation).