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Series-B fintech, Bangalore location, and broadly sought data-engineering skills drive moderate competition.
Core data engineering skills are transferable, but fintech multi-tenancy and data-security requirements increase domain specificity.
Explicit 7–10 years and many mandatory technologies (Spark, Kafka, ClickHouse, AWS, Terraform) increase screening strictness.
Design and scale end-to-end data platforms supporting batch and real-time workloads in a high-growth fintech environment.
Own data architecture across ingestion, streaming, storage, processing, analytics, security, and reliability.
Mentor engineers and establish data architecture standards while driving infrastructure and cost optimization on AWS.
7–10 years of strong hands-on Data Engineering experience with architectural ownership.
Expertise in distributed systems: Spark, Flink, Kafka.
Hands-on experience with AWS data services including S3, Glue, RDS, MSK, Redshift, DMS, and Athena.
Experience with ClickHouse, BigQuery, Redshift, or Snowflake; and Terraform/CloudFormation for infrastructure as code.
Proven ability to build data platforms from ground up at significant scale in fintech or similar domains.
Strong skills in data modeling (Star Schema, Data Vault 2.0, OBT) coupled with expertise in streaming, CDC, dbt, Python, SQL, and Airflow.
Experienced in designing multi-tenant architectures emphasizing security, IAM, encryption, VPC, and compliance.