Technologies / Skills Advanced SQL, Python and associated libraries like Pandas, Numpy etc., Pyspark , Shell scripting, DataModelling, Big data, Hadoop , Hive, ETL pipelines and IaC tools like Terraform etc.

Responsibilities Proven success in communicating with users, other technical teams, and senior management to Collect requirements, describe data modeling decisions and develop data engineering strategy.

Ability to work with business owners to define key business requirements and convert to user stories with required technical specifications.

Communicate results and business impacts of insight initiatives to key stakeholders to collaboratively solve business problems.

Working closely with the overall Enterprise Data & Analytics Architect and Engineering practice leads to ensure adherence with the best practices and design principles.

Assures quality, security and compliance requirements are met for supported area.

Design and create fault-tolerance data pipelines running on cluster

Excellent communication skills with the ability to influence client business and IT teams

Should have design data engineering solutions end to end. Ability to come up with scalable and modular solutions Required Qualification 7 years of relevant IT experience





4+ years of hands-on experience Designing and developing Data Pipelines for Data Ingestion or Transformation using Python (PySpark)/Spark SQL in AWS cloud

Experience in design and development of data pipelines and processing of data at scale using technologies like EMR, Kinesis, Kafka, Lambda, Glue, Athena, Redshift, Step Functions.

Advanced experience in writing and optimizing effective SQL queries with Python and Hive handling Large Data Sets in Big-Data Environments

Experience in debugging, tunning and optimizing PySpark data pipelines

Should have implemented concepts and have good knowledge of Pyspark dataframes, joins, caching, memory management, partitioning, parallelism etc.

Understanding of Spark UI, Event Timelines, DAG, Spark config parameters, in order to tune the long running data pipelines.

Experience working in Agile implementations

Experience with building data pipelines in streaming and batch mode.

Experience with Git and CI/CD pipelines to deploy cloud applications

Good knowledge of designing Hive tables with partitioning for performance Desired Qualification Experience in data modelling

Hands on creating workflows on any Scheduling Tool like Autosys, CA Workload Automation

Proficiency in using SDKs for interacting with native AWS services

Strong understanding of concepts of ETL, ELT and data modeling

📌 Lead Data Engineer (Mumbai)
🏢 Go Digital Technology Consulting
📍 Mumbai