Senior Data Engineer, Databricks Migration
Sigma Software GroupMigrate enterprise retail analytics from BigQuery to Databricks Lakehouse, building scalable PySpark pipelines, medallion architecture, and governed cloud data platforms.
Atidaryti
„Intetics“ yra individualizuotos programinės įrangos kūrimo ir konsultavimo įmonė, padedanti organizacijoms stiprinti technologinius pajėgumus pasitelkiant dirbtinio intelekto, mašininio mokymosi, programinės įrangos inžinerijos ir duomenų apdorojimo paslaugas. Jos „Remote In-Sourcing®“ modelis padeda suburti specializuotas ekspertų komandas, o TETRA™ suteikia programinės įrangos kokybės vertinimo įrankius. Įmonė taiko šiuolaikines technologijas ir aukštos kokybės duomenų valdymo praktiką sveikatos priežiūros, finansų, logistikos ir kitose srityse.
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.
Aprašymas
- 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.
Reikalavimai
- 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.
Susiję darbai
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