Job Description :

- 3+ years of experience in analytics, Pyspark, Python, Spark, SQL and associated data engineering jobs.

- Must have experience with managing and transforming big data sets using pyspark, spark-scala, Numpy pandas.

- Excellent communication & presentation skills.

- Experience in managing Python codes and collaborating with customer on model evolution.

- Good knowledge of data base management and Hadoop/Spark, SQL, HIVE, Python (expertise).

- Superior analytical and problem solving skills.

- Should be able to work on a problem independently and prepare client ready deliverable with minimal or no supervision.

- Good communication skill for client interaction.

Data Management Skillsets :

- Ability to understand data models and identify ETL optimization opportunities. Exposure to ETL tools is preferred.

- Should have strong grasp of advanced SQL functionalities (joins, nested query, and procedures).

- Strong ability to translate functional specifications / requirements to technical requirements.

Required Skillset :

- Demonstrated expertise in building distributed data processing systems using PySpark and Python, with a deep understanding of data modeling and schema design.

- Proven ability to architect and deploy cloud-native data solutions within the AWS ecosystem, ensuring high availability and fault tolerance.

- Strong analytical mindset with the ability to translate complex business requirements into technical specifications that drive tangible project outcomes.

- Excellent communication skills, with the ability to articulate technical concepts to non-technical stakeholders and work effectively in a collaborative, hybrid-work environment.

- A degree in Computer Science, Engineering, or a related quantitative field, combined with 3 to 7 years of professional experience in data engineering roles.

- High degree of adaptability to work across time zones in a fast-paced, global MNC environment, with a commitment to continuous learning and technical excellence.

Key Skills :

- Pyspark, AWS, Data Ingestion, Data Transformation, Data Engineering, Bigquery, Hadoop, Big Data, Aws Glue, SQL, Data Integration, Hive, Data Migration, Data Pipeline, Big Data Technologies, Data Warehousing, Spark, Data Modeling, ETL, Python.