Overview

Our Story At Alight, we believe a company’s success starts with its people.

Responsibilities

• Build and maintain high volume ETL/ELT pipelines across Hadoop (HDFS, Hive, Spark, Kafka) and AWS (Glue, EMR, Lambda, Step Functions, Redshift).
• Develop distributed data processing solutions using PySpark, Spark SQL, and scalable cloud serverless patterns.
• Implement reusable data ingestion frameworks for batch, ability to design & implement Orchestration process and Leverage AI
• Optimize data workflows using partitioning, bucketing, compression, file formats (Parquet/ORC).
• Understanding hybrid data lake architectures using S3 + HDFS, ensuring data governance and best practices are adheres

Qualifications

• Leverage extensive experience (4 to 8 years overall ETL experience, to assist in solution design and delivery along with build of new ETLs).
• We are seeking an experienced ETL Developer with strong expertise in Big Data (Spark, Cloudera).
• Ability to implement end-to-end serverless data architectures integrating Glue, Lambda, S3, and Redshift
• Assist in Design and build the robust, scalable and secure software solutions across the having no/least adoption
• Define clear technical specifications and make architecture decisions that align with business goals and long-term scalability.
• Implement best practices (including secure code guidelines) through the implementation of unit tests, automation, leverage and code reviews. Drive continuous improvement in code quality and maintainability.
• Troubleshooting issues and proactively solving problems as they arise, ensuring the smooth operation of full stack applications
• Ability to understand the data flow diagram, data modelling and Lineages
• Job orchestration using Airflow, Control M, Step Functions, or event-driven triggers.
• Ensure data is protected and compliant with regulatory standards.
• Work closely with business stakeholders to enable high quality datasets.
• Work on best practice adoption and provide guidance to peers/juniors in team.
• Ability to respond on incidents, and troubleshooting Spark performance issues, job failures, and cluster bottlenecks.
• Collaborate closely with team members, QA and cross product teams to streamline release processes.
• Collaborate with business stakeholders to gather, analyse, and translate data into technical solutions
• Strong experience with the AWS data stack (S3, Glue, EMR, Lambda, Kinesis, Redshift, Step Functions etc.,).
• Strong hands-on expertise in Scala, PySpark, Spark optimization techniques, HiveQL, and distributed computing.
• Good understanding of Hadoop ecosystem (HDFS, Hive, Spark, YARN, Kafka).
• Good
• Proficiency in at least one scripting/programming language: Python, Shell scripting.
• Strong experience with CI/CD, GitHub, Git commands.
• Expertise in ETL and Data Warehousing and cloud concepts.
• Good understanding of data modelling (star/snowflake), partitioning strategies, and schema evolution.
• Expertise in data profiling and decision making.
• Able to understand, design and create data flow diagrams.
• Able to understand the architecture and design end-to-end data flow.
• Hands-on experience with Airflow, or Control‑M, or other orchestrators.
• To monitor and support BAU and year end activities, if needed.
• Exposure to security and compliance aspects in Cloud.
• Familiarity with serverless patterns and containerization (Docker, ECS/EKS).