Job
Description
Role Overview We are seeking a highly skilled and forward-thinking professional to lead our Data Engineering and Data Science initiatives. As a Lead – DE + DS , you will play a critical role in designing and scaling data pipelines, architecting data platforms, and developing predictive models that drive strategic decision-making across the organization. This is a hybrid leadership role combining hands-on technical expertise with people management and stakeholder engagement. Key Responsibilities Data Engineering: Architect and manage scalable and secure data pipelines and ETL/ELT processes using cloud-based platforms (e.g., AWS, Azure, GCP) Design and maintain data lake/data warehouse structures and ensure data quality, availability, and governance Collaborate with DevOps and platform teams to automate data workflows and deploy pipelines in production Data Science Lead the development, deployment, and monitoring of machine learning models for business use cases (e.g., forecasting, recommendation engines, anomaly detection) Drive experimentation and advanced analytics using statistical, machine learning, and deep learning methods Translate business problems into data-driven solutions and actionable insights Leadership & Collaboration Lead and mentor a team of data engineers and data scientists, fostering skill development and collaboration Partner with business stakeholders, product owners, and engineering teams to align on data strategies and deliver impactful outcomes Define and enforce best practices in data architecture, coding standards, and model lifecycle management Required Skills & Qualifications Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field 8+ years of relevant experience in data engineering and/or data science, with at least 2 years in a technical leadership role Proficiency in SQL, Python, Spark, and distributed data processing frameworks Experience with data warehousing (Snowflake, Redshift, BigQuery), and data pipeline tools (Airflow, dbt, etc.) Strong understanding of ML frameworks (Scikit-learn, TensorFlow, PyTorch) and model deployment practices Solid grasp of data governance, MLOps, and CI/CD practices in a cloud environment Excellent communication and stakeholder management skills Preferred Qualifications Experience in Agile delivery environments Certifications in cloud platforms (e.g., AWS Certified Data Analytics, GCP Professional Data Engineer) Exposure to real-time data streaming (Kafka, Kinesis, etc.) Familiarity with visualization tools like Power BI, Tableau, or Looker Show more Show less
