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Software Engineer, Infrastructure (Platform) - Biotech Software Leader

Andiamo

New York · On-site Full-time Lead 1w ago

About the role

Big Data Software Engineer — Streaming

About The Role

Build real-time data systems for high-cardinality, high-throughput streams. You’ll design ingestion, processing, and low-latency delivery for analytics and ML.

Responsibilities • Design streaming topologies (Kafka/Flink/Spark Structured Streaming) with exactly-once semantics. • Implement schema management, data contracts, and backward-compatible evolution. • Optimize stateful processing, watermarking, and checkpointing for resiliency. • Expose durable, queryable topics and near-real-time views (Materialize/ClickHouse/Druid).

Requirements • 4+ years in distributed data systems; strong JVM or Scala/Python skills. • Deep knowledge of streaming guarantees, partitioning, and storage formats (Parquet/Avro).

About Andiamo

Talent Partners for the AI Revolution. As a globally recognized staffing and consulting firm, we specialize in placing the top 2% of technology and go-to-market professionals with the world’s largest and most well-known companies.

For over 20 years, we've maintained the status of tier-one vendor for firms such as Palantir, Amazon, Fluidstack, Bloomberg, Relativity Space, Firefly, MasterCard, Visa, Two Sigma, Citadel, as well as other major financial services firms, elite hedge funds, Google-backed tech start-ups, and major software firms.

Our talent solutions include Permanent Placement, Contract Staffing, Executive Search, and Dedicated Recruiting Services (RPO). Find out more at www.andiamogo.com

Requirements

  • 4+ years in distributed data systems; strong JVM or Scala/Python skills
  • Deep knowledge of streaming guarantees, partitioning, and storage formats (Parquet/Avro)

Responsibilities

  • Build real-time data systems for high-cardinality, high-throughput streams
  • You’ll design ingestion, processing, and low-latency delivery for analytics and ML
  • Design streaming topologies (Kafka/Flink/Spark Structured Streaming) with exactly-once semantics
  • Implement schema management, data contracts, and backward-compatible evolution
  • Optimize stateful processing, watermarking, and checkpointing for resiliency
  • Expose durable, queryable topics and near-real-time views (Materialize/ClickHouse/Druid)

Benefits

null

Skills

KafkaFlinkSpark Structured StreamingJVMScalaPythonParquetAvroMaterializeClickHouseDruid

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