Data Engineer

Role & responsibilities

Design, develop, and implement robust data pipelines for seamless data ingestion, transformation, and storage. • Build and deploy Machine Learning models to deliver actionable insights and predictive analytics. • Conduct Exploratory Data Analysis (EDA) on structured and unstructured data. • Optimize and manage big data environments ensuring performance, scalability, and reliability. • Collaborate closely with cross-functional teams (engineering, product, business) to deliver data-driven solutions aligned with business goals. • Uphold industry standards in Data Governance, Security, and Compliance. • Tune performance and optimize SQL/NoSQL databases and cloud-based storage systems. • Lead data visualization initiatives, transforming analytical outputs into digestible and actionable dashbo ards or reports. • Collect, clean, and analyze large datasets from multiple sources. • Develop and maintain dashboards, visualizations, and reports for business tracking. • Extract, manipulate, and analyze data using SQL, Python, or R. • Identify trends, anomalies, and insights that impact business performance. • Conduct market research, customer behavior analysis, and performance tracking. • Ensure data integrity and maintain compliance with data quality standards and policies.

Required Qualifications & Experience • Bachelors or Masters degree in Computer Science, Data Science, Statistics, or a related field. • 5+ years of hands-on experience in Data Engineering or related domains. • Proficiency in programming languages such as Python, SQL, Scala, or Java. • Deep knowledge of Big Data tools: Apache Spark, Hadoop, and frameworks like Apache Airflow, Kafka. • Experience in Cloud Platforms: AWS, Azure, or Google Cloud Platform (GCP). • Practical experience with Machine Learning libraries/frameworks: TensorFlow, PyTorch, Scikit-learn. • Skilled in data visualization tools: Tableau, Power BI, or similar platforms.

Strong analytical mindset with excellent problem-solving and communication skills. Preferred Skills (Good to Have) • Familiarity with DevOps practices and containerization tools such as Docker and Kubernetes. • Exposure to real-time data streaming and analytics. • Knowledge in advanced fields like Deep Learning, Natural Language Processing (NLP), or Computer Vision