Senior Analyst

Data Pipeline Development & Engineering

• Design, build, and maintain scalable, reliable, and efficient data pipelines to support analytics and reporting needs

• Develop and manage ETL/ELT workflows using Apache Airflow to orchestrate complex data movement and transformation processes
• Optimize data ingestion, transformation, and loading processes to ensure timely and accurate data availability

• Troubleshoot and resolve pipeline failures, data quality issues, and performance bottlenecks

Data Platform & Infrastructure Management

• Work with large-scale distributed data platforms including Hive and Presto for data storage, querying, and processing

• Manage and optimize data warehouses and data lake architectures on AWS (S3, Redshift, Glue, EMR, Lambda, etc.)
• Ensure high availability, scalability, and performance of data infrastructure

• Implement data partitioning, indexing, and query optimization strategies to improve performance and reduce cost

Process Excellence & Automation

• Identify process gaps and implement automation solutions to improve data engineering workflows and operational efficiency

• Standardize pipeline development practices, code quality standards, and deployment processes
• Leverage Gen AI / Agentic AI capabilities to automate repetitive data engineering tasks, accelerate development, and improve pipeline reliability

• Drive continuous improvement initiatives across data engineering operations

Data Quality & Governance

• Implement robust data validation, quality checks, and monitoring frameworks across pipelines

• Ensure data accuracy, consistency, and integrity across all data sources and reporting systems
• Collaborate with analytics and business teams to define and enforce data governance standards

• Maintain comprehensive documentation for data models, pipelines, and data dictionaries

Technical Development & Advanced Analytics Support

• Perform advanced data extraction, transformation, and analysis using Python and SQL

• Build reusable data models and transformation logic to support multiple analytics use cases
• Work with structured and unstructured datasets from diverse sources including transactional systems, marketing platforms, and third-party APIs

• Support data scientists and analysts by providing clean, well-modeled, and readily accessible datasets

Cross-Functional Collaboration

• Partner with Data Analytics, Product, Marketing, Operations, and Technology teams to gather data requirements and deliver engineering solutions

• Drive alignment and execution across multiple stakeholders and geographies
• Translate complex technical concepts and data architecture decisions clearly to non-technical leadership

• Manage multiple priorities in a fast-paced and dynamic environment

Requirements

• Minimum 3–6 years of experience in Data Engineering or a related field

• BE/B.Tech/B.Sc/Masters Degree in Computer Science, Engineering, or a relevant field
• Strong hands-on experience with Apache Airflow for pipeline orchestration is mandatory
• Expertise in working with API’s to retrieve data.
• Deep expertise in Python ,SQL & PowerBI for data engineering tasks is mandatory
• Hands-on experience with Hive and Presto for large-scale data processing and querying
• Strong proficiency in AWS services including S3, Glue, EMR, Redshift, Lambda, and IAM
• Strong proficiency in Gen AI / Agentic AI tools and their application in data engineering workflows
• Solid understanding of data modeling, ETL/ELT concepts, and data warehouse/data lake architectures
• Proven ability to drive execution across multiple stakeholders and geographies
• Experience in building and maintaining production-grade data pipelines

• Excellent problem-solving and critical-thinking skills

Preferred Skills

• Experience with real-time/streaming data pipelines (Kafka, Spark Streaming, Kinesis)

• Exposure to data quality frameworks and observability tools (Great Expectations, Monte Carlo, etc.)
• Familiarity with dbt for data transformation and modeling
• Experience with Infrastructure as Code (Terraform, CloudFormation)
• Understanding of statistical and analytical concepts to better support data science teams

• Exposure to marketing, customer, or product data domains

Behavioral Competencies

• Strong ownership and accountability mindset

• Structured thinker with high attention to detail and a passion for data quality
• Excellent stakeholder management and communication skills
• Ability to work in ambiguous and high-pressure environments
• Strong collaboration and execution-oriented approach

• Continuous learning mindset with genuine interest in emerging data and AI technologies