Data Engineer

<div>C1X AdTech Pvt Ltd is a fast-growing <strong>product and engineering-driven AdTech company</strong> building next-generation advertising and marketing technology platforms. Our mission is to empower enterprise clients with the smartest marketing solutions, enabling seamless integration with personalization engines and delivering cross-channel marketing capabilities. We are dedicated to enhancing customer engagement and experiences while focusing on increasing Lifetime Value (LTV) through consistent messaging across all channels.</div><div>Our engineering team spans <strong>front end (UI), back end (Java/Node.js APIs), Big Data, and DevOps</strong>, working together to deliver scalable, high-performance products for the digital advertising ecosystem.</div><div><strong>Role Overview</strong><br>As a <strong>Data Engineer</strong>, you will be a key member of our data engineering team, responsible for building and maintaining large-scale data products and infrastructure. You’ll shape the next generation of data analytics tech stack by leveraging modern big data technologies. This role involves working closely with business stakeholders, product managers, and engineering teams to meet diverse data requirements that drive business insights and product innovation.</div><div align="center" style="text-align:center;"><hr align="center"></div><div><strong>Objectives</strong></div><ul style="margin-top:0cm;" type="disc"><li>Design, build, and maintain scalable <strong>data infrastructure</strong> for collection, storage, and processing.</li><li>Enable easy access to reliable data for data scientists, analysts, and business users.</li><li>Support <strong>data-driven decision-making</strong> and improve organizational efficiency through high-quality data products.</li></ul><div align="center" style="text-align:center;"><hr align="center"></div><div><strong>Responsibilities</strong></div><ul style="margin-top:0cm;" type="disc"><li>Build large-scale <strong>batch and real-time data pipelines</strong> using frameworks like <strong>Apache Spark</strong> on AWS or GCP.</li><li>Design, manage, and automate <strong>data flows</strong> between multiple data sources.</li><li>Implement best practices for <strong>continuous integration, testing, and data quality assurance</strong>.</li><li>Maintain data documentation, definitions, and governance practices.</li><li>Optimize performance, scalability, and cost-effectiveness of data systems.</li><li>Collaborate with stakeholders to translate business needs into data-driven solutions.</li></ul><div align="center" style="text-align:center;"><hr align="center"></div><div><strong>Qualifications</strong></div><ul style="margin-top:0cm;" type="disc"><li>Bachelor’s degree in Computer Science, Engineering, or related field (exceptional coding performance on platforms like LeetCode/HackerRank may substitute).</li><li><strong>2+ years’ experience</strong> working on full lifecycle Big Data projects.</li><li>Strong foundation in <strong>data structures, algorithms, and software design principles</strong>.</li><li>Proficiency in at least <strong>two programming languages</strong> – Python or Scala preferred.</li><li>Experience with <strong>AWS services</strong> such as EMR, Lambda, S3, DynamoDB (GCP equivalents also relevant).</li><li>Hands-on experience with <strong>Databricks Notebooks</strong> and Jobs API.</li><li>Strong expertise in big data frameworks: <strong>Spark, MapReduce, Hadoop, Sqoop, Hive, HDFS, Airflow, Zookeeper</strong>.</li><li>Familiarity with <strong>containerization (Docker)</strong> and <strong>workflow management tools (Apache Airflow)</strong>.</li><li>Intermediate to advanced knowledge of <strong>SQL</strong> (relational + NoSQL databases like Postgres, MySQL, Redshift, Redis).</li><li>Experience with <strong>SQL tuning, schema design, and analytical programming</strong>.</li><li>Proficient in <strong>Git</strong> (version control) and collaborative workflows.</li><li>Comfortable working across diverse technologies in a <strong>fast-paced, results-oriented environment</strong>.</li></ul>