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Tier-1 brand, metro location, mid-level generalist data role with broad Spark/Kafka/Airflow requirements.
Specialized big-data and ML pipeline skills reduce cross-industry transferability.
Explicit 5–7 years plus mandatory Spark, Airflow, Kafka and cloud big-data skills.
Design, automate, and optimize end-to-end data science and big data pipelines for analytics platforms.
Develop and deploy scalable data solutions including Customer 360 and machine learning feature stores across large, complex datasets.
Collaborate with business stakeholders and technical teams to translate business challenges into data models and oversee performance monitoring of machine learning algorithms.
5 to 7 years of experience working with large datasets and distributed computing tools, including Apache Spark.
Experience with big data technologies such as Apache Airflow, Kafka, Sqoop, Flume, or NiFi, and familiarity with distributed storage platforms like AWS, GCP, or HDFS.
Programming skills in Scala, Python, Java, or R and experience with NoSQL databases such as Cassandra, MongoDB, HBase, or Redis.
Work Experience Required: 5 to 7 years; Notice Period: Not explicitly mentioned in the JD.
Experienced in building and optimizing machine learning pipelines and real-time analytics solutions in cloud or large distributed environments.
Capable of leading technical discussions on data architecture and providing guidance on metadata and data model management.
Skilled in interdisciplinary collaboration with data scientists, database teams, technologists, and business stakeholders to deliver end-to-end analytics solutions.