Data Engineer

Job Role: Data Engineer Location: Hyderabad, India Experience: 3+ Years POSITION OVERVIEW Anblicks is seeking a motivated and technically skilled Data Engineer to design, develop, and maintain reliable data pipelines that transform large volumes of data into high-quality, scalable, and analytics-ready datasets. The ideal candidate will have hands-on experience with SQL, Python, ETL/ELT development, and modern cloud data platforms. You will collaborate with senior engineers, Data Scientists, and cross-functional teams to integrate data from multiple sources, implement data quality checks, optimize pipeline performance, and ensure data reliability, security, and governance. The role also offers opportunities to work with modern data engineering practices, cloud technologies, and emerging AI-assisted development tools. DUTIES AND RESPONSIBILITIES Design, develop, and maintain scalable ETL/ELT pipelines to ingest, transform, standardize, and process data from multiple sources. Write efficient SQL queries and develop data processing solutions using Python, Scala, Java, or other relevant programming languages. Implement data validation, quality checks, monitoring, and error-handling mechanisms to ensure data accuracy and reliability. Integrate data from external systems, partner platforms, APIs, and structured or semi-structured data sources. Work with cloud and data platforms such as Snowflake, Databricks, and AWS to build and maintain data processing workflows. Support data modeling, transformation, and preparation activities to make data available for analytics, reporting, and machine learning use cases. Identify and troubleshoot data pipeline failures, performance bottlenecks, and data inconsistencies. Follow data security, privacy, access control, and governance practices while handling sensitive or business-critical data. Collaborate with Data Engineers, Data Scientists, and other stakeholders to understand requirements and deliver reliable data solutions. Maintain technical documentation, pipeline specifications, operational runbooks, and data lineage information. Use orchestration tools and, where applicable, streaming technologies to automate and manage data workflows. Explore AI-assisted development tools to improve productivity while validating generated code for accuracy, security, and maintainability. QUALIFICATIONS Required Qualifications Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field. 2+ years of hands-on experience in Data Engineering, ETL/ELT development, or a related data-focused role. Strong proficiency in SQL, including complex queries, joins, aggregations, and query optimization. Hands-on experience with at least one programming language, preferably Python, Scala, or Java. Experience developing, maintaining, or troubleshooting ETL/ELT data pipelines. Familiarity with cloud platforms and modern data technologies such as Snowflake, Databricks, or AWS. Understanding of data modeling, data transformation, data quality, and pipeline monitoring. Basic knowledge of data security, privacy, and governance practices. Strong analytical, problem-solving, communication, and collaboration skills. Preferred Qualifications Exposure to identity resolution, entity matching, record linkage, graph-based data processing, or graph databases. Experience in integrating external data sources through APIs, partner feeds, or third-party platforms. Familiarity with workflow orchestration tools such as Apache Airflow or similar technologies. Exposure to streaming and event-processing technologies such as Apache Kafka or AWS Kinesis. Familiarity with data clean rooms, privacy-preserving data processing, or consent-based data handling. Experience working in data-driven product environments, preferably in AdTech, MarTech, or digital analytics. Exposure to full-stack development, including front-end and back-end technologies, beyond core data engineering. Familiarity with LLMs, RAG, MCP, and AI-assisted development tools such as GitHub Copilot or Claude Code. Understanding of best practices for validating AI-generated code and using AI-assisted development workflows responsibly.