Master Data Management (MDM): Design and implement MDM strategies to ensure data consistency, quality, and accuracy across multiple systems. ETL Development: Develop, maintain, and optimize ETL pipelines to extract, transform, and load data from various sources into data warehouses or data lakes. AWS Services: Utilize AWS cloud technologies (such as Redshift, S3, Lambda, and EC2) to support scalable and cost-effective data storage and processing solutions. Data Analysis & Reporting: Develop interactive dashboards and reports in Tableau to visualize key business metrics and provide actionable insights to stakeholders. Python & SQL Development: Write efficient Python scripts and SQL queries to process, manipulate, and analyze large datasets. Data Integration: Integrate data from various internal and external systems, ensuring seamless data flows and supporting business needs. Data Quality Assurance: Ensure high data quality standards are met by performing regular data validation, cleansing, and enrichment processes. Collaboration: Work closely with cross-functional teams (data scientists, business analysts, IT) to understand business requirements and translate them into effective data solutions. Performance Optimization: Continuously monitor and optimize data processing and reporting systems to improve efficiency and reduce processing time. Documentation: Maintain comprehensive documentation for ETL processes, data pipelines, and dashboard designs.

Job Summary: We are seeking a highly skilled Lead Data Engineer/Associate Architect to lead the design, implementation, and optimization of scalable data architectures. The ideal candidate will have a deep understanding of data modeling, ETL processes, cloud data solutions, and big data technologies. You will work closely with cross-functional teams to build robust, high-performance data pipelines and infrastructure to enable data-driven decision-making. Experience: 7 - 12 years Work Location: Hyderabad (Hybrid) / Remote Mandatory skills: AWS, Python, SQL, Airflow, DBT Must have done 1 or 2 projects in Clinical Domain/Clinical Industry. Responsibilities: Design and develop scalable and resilient data architectures that support business needs, analytics, and AI/ML workloads. Data Pipeline Development: Design and implement robust ETL/ELT processes to ensure efficient data ingestion, transformation, and storage. Big Data & Cloud Solutions: Architect data solutions using cloud platforms like AWS, Azure, or GCP, leveraging services such as Snowflake, Redshift, BigQuery, and Databricks. Database Optimization: Ensure performance tuning, indexing strategies, and query optimization for relational and NoSQL databases. Data Governance & Security: Implement best practices for data quality, metadata management, compliance (GDPR, CCPA), and security. Collaboration & Leadership: Work closely with data engineers, analysts, and business stakeholders to translate business requirements into scalable solutions. Technology Evaluation: Stay updated with emerging trends, assess new tools and frameworks, and drive innovation in data engineering. Required Skills: Education: Bachelor's or master's degree in computer science, Data Engineering, or a related field. Experience: 7 - 12+ years of experience in data engineering Cloud Platforms: Strong expertise in AWS data services. Databases: Hands-on experience with SQL, NoSQL, and columnar databases such as PostgreSQL, MongoDB, Cassandra, and Snowflake. Programming: Proficiency in Python, Scala, or Java for data processing and automation. ETL Tools: Experience with tools like Apache Airflow, Talend, DBT, or Informatica. Machine Learning & AI Integration (Preferred): Understanding of how to architect data solutions for AI/ML applications