Responsibilities

• Design, implement, and optimize data pipelines for batch and real-time data processing using Cloudera (Hadoop, Hive, Spark, Impala) and Informatica (PowerCenter, Cloud Data Integration)

• Build data extraction, transformation, and loading (ETL) workflows using Informatica PowerCenter for large-scale data integration from source systems (e.g., relational databases, flat files, APIs) into Cloudera Data Lake or data warehouse environments.

• Implement Spark jobs on Cloudera for distributed data processing and optimization of data workflows.

• Leverage Informatica for orchestrating ETL workflows, including data extraction, cleansing, transformation, and loading into data repositories (HDFS, Hive, SQL databases, etc.).

• Optimize the Informatica workflows to minimize runtime, ensure smooth data integration, and maintain high data quality.

• Utilize Hadoop and Spark on Cloudera to process large datasets and implement data transformations using MapReduce, Spark SQL, and PySpark.

• Leverage Impala for low-latency SQL queries on Hadoop, ensuring real-time access to processed data.

• Implement partitioning, bucketing, and indexing strategies in Hive and HBase to improve query performance on large datasets.

• Implement and enforce data quality rules within Informatica workflows, ensuring that all transformations meet the required standards for completeness, consistency, and accuracy.

• Ensure compliance with data governance and security protocols (e.g., encryption, masking, access control) in accordance with industry best practices.

• Automation and Scheduling: Automate ETL workflows using Informatica Server, integrating with Airflow, Nifi or other workflow orchestration tools for scheduling and monitoring jobs.

• Utilize Cloudera Navigator for monitoring and auditing data processes within the Hadoop ecosystem.

• Perform regular tuning of the ETL pipelines, data flows, and SQL queries to ensure optimal performance.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or related field.

• 6+ years of experience in the same field.

• Proven experience with the Cloudera Distribution of Hadoop (CDH), including expertise in HDFS, Hive, Impala, Spark, and HBase.

• Strong hands-on experience with Informatica PowerCenter (ETL), EDC, IDQ, B2B, and Axon.

• Deep understanding of ETL best practices, data pipelines, and distributed computing technologies such as Spark, MapReduce, PySpark, and Hadoop ecosystem components.

• Advanced SQL skills for data manipulation, aggregation, optimization, and reporting across relational and non-relational data stores (e.g., SQL Server, MySQL, PostgreSQL, Hive, Impala).

• Experience in Python and SQL.

• Strong background in data warehousing principles and data modeling, including dimensional modeling (star schema, snowflake schema) and OLAP/OLTP considerations.