Senior Real-Time Data Engineer

150 - 200 PLN/ godz./ godz.B2BB2B (netto)

SeniorHybryda·KrakówInne·B2B

#466270·Dodano 2 dni temu·0

Źródło: justjoin.it

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Tech Stack / Keywords

Python KafkaFlinkEvent StreamingSQL

Podsumowanie oferty

Build real-time data processing and streaming infrastructure for sports data platforms, analytics, and event-driven projects. Work includes high-volume data ingestion and delivery using Python, Apache Kafka, Apache Flink, and SQL.

Wymagania

5+ years of professional experience in Data Engineering or backend/data platform development.
Strong Python and SQL skills.
Hands-on experience building production-grade streaming or event-driven data pipelines.
Experience with Apache Kafka or comparable messaging technologies.
Practical knowledge of Apache Flink, Spark Structured Streaming, or similar frameworks.
Solid understanding of distributed systems and asynchronous processing.
Experience integrating external APIs and data sources.
Understanding of data quality, consistency, and failure recovery.
Experience with cloud infrastructure.
Good English.
Ability to work with technical and business stakeholders.

Strong Advantage:

Commercial experience with sports data, betting platforms, or sportsbook technology.
Experience with live sports event feeds and real-time match data.
Experience with odds feeds, market data, or dynamic pricing systems.
Experience with low-latency or high-throughput data architectures.
Experience with real-time trading or financial market data platforms.
Experience with event-time processing, windowing, and stream state management.
Experience with Kafka Streams, Flink, or similar streaming technologies.
Experience with Redis, ClickHouse, or other high-performance data stores.
Experience with AWS Kinesis, Google Pub/Sub, or managed streaming services.
Experience with data pipelines supporting machine learning and predictive models.
Experience with data reconciliation, feed reliability, and event deduplication.

Obowiązki

Build real-time ingestion and processing for high-volume event data.
Develop streaming pipelines supporting live sports data and analytics.
Integrate external data feeds, APIs, and event sources.
Normalize, validate, and enrich data.
Work with event-driven architectures and distributed data processing.
Deliver low-latency data to analytical and predictive systems.
Handle late, duplicated, missing, or out-of-order events.
Develop real-time and historical data storage solutions.
Monitor production data pipelines and support their observability and reliability.
Collaborate with Data Scientists, Backend Engineers, and domain specialists.

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