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Sigma Software GroupMigrate enterprise retail analytics from BigQuery to Databricks Lakehouse, building scalable PySpark pipelines, medallion architecture, and governed cloud data platforms.
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ML Data Engineer, Romania Remote
Build real-time, graph, and time-series data infrastructure for athlete performance. Support machine learning evaluation and AI systems for an Intetics customer in the United States.
विवरण
- Build production data infrastructure for large volumes of athlete performance and sensor data.
- Develop and operate real-time and near-real-time streaming data ingestion systems.
- Design storage and data architectures for large time-series datasets and longitudinal athlete records.
- Provide low-latency access to production features and derived metrics for machine learning systems and AI agents.
- Create graph models and schemas linking athletes with training loads, injuries, performance, and outcomes.
- Develop data and ML evaluation systems to assess model reliability, calibration, and real-world performance.
- Maintain tenant-level data isolation across clubs and customers.
- Establish data provenance, lineage, auditability, and observability across the platform.
- Partner with ML and AI engineers to support model training, inference, and evaluation with reliable data.
- Translate complex requirements into durable production systems with sport scientists and domain experts.
- Make practical technology and architecture decisions as the platform evolves.
- Collaborate with Intetics’ US-based customer.
आवश्यकताएँ
- At least five years of full-time professional software or data engineering experience; internships, university placements, coursework, and academic projects do not count.
- Demonstrated experience designing, building, and operating production data infrastructure at scale.
- Strong experience with time-series data or databases such as InfluxDB, TimescaleDB, Prometheus, ClickHouse, or equivalent.
- Substantial experience with real-time or streaming ingestion using Kafka, Kinesis, Flink, Spark Streaming, Pulsar, or equivalent.
- Experience designing graph data models or database schemas, beyond querying or using an existing graph database.
- Experience designing or operating multi-tenant systems with tenant-level data isolation.
- Strong Python and SQL skills.
- Professional Go experience is highly desirable.
- Experience with production systems where reliability, scalability, observability, and data accuracy are essential.
- Ability to own ambiguous technical problems and shape them into practical production architectures.
- Experience working directly with data scientists, ML engineers, or other technical domain specialists.
- Experience building probabilistic evaluation, model calibration, or model monitoring infrastructure.
- Experience with causal inference, counterfactual modeling, or simulation.
- Experience with wearable sensors, IoT data, biomechanics, sports technology, or other high-frequency telemetry.
- Experience building knowledge graphs or domain-specific ontologies.
- Experience with LLM or AI evaluation frameworks and awareness of their limitations.
- Experience with AWS services such as ECS, EC2, Lambda, SNS, SQS, or related tools.
- Experience with GraphQL, REST, gRPC, Postgres, MongoDB, or similar technologies.
- English proficiency at B2 level or above.
- Must be located in the EU.
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