Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

We are looking for a Data engineer to join a technical discovery team working on a constrained wireless sensor network.
The role focuses on turning heterogeneous network telemetry into a reliable, analysis-ready representation of network behavior over time. You will work closely with Network Architects, Network/Embedded Engineers and ML Engineers to assess data readiness, reconstruct network states, establish data-quality baselines and prepare datasets for network analytics, simulation and ML feasibility experiments.
This is not a traditional data-platform role. We are looking for someone comfortable working with time-series telemetry, event data, distributed systems, network data and imperfect real-world datasets.
The initial engagement is a discovery and feasibility phase, with the potential to continue into implementation of selected network intelligence capabilities.

Telemetry & Data assessment

– Inventory available network telemetry, historical data, APIs and simulator outputs
– Analyze telemetry schemas, identifiers, timestamps and data relationships
– Assess historical coverage, completeness, consistency and data quality
– Identify missing fields, unreliable measurements and data gaps
– Determine what additional instrumentation or event labeling may be required
– Work with Network Engineers to understand the semantics of telemetry generated by the network

Data integration & Normalization

– Extract and normalize data from multiple telemetry sources
– Join data across devices, gateways, repeaters, links and network events
– Align heterogeneous time-series data using device/network identifiers and timestamps
– Correlate telemetry with: topology changes, route changes, link failures, acknowledgements, reconnections, packet loss, node availability, battery state, network load
– Design reliable schemas for network telemetry and event data

Network-state dataset construction

– Build an analysis-ready representation of network state over time.
– Represent network snapshots/events including: nodes, links, paths/routes, topology, capacity, load, link quality, node state
– Develop datasets suitable for offline analysis, experimentation and future ML model development
– Support construction of temporal and event-based datasets for network degradation, capacity and maintenance hypotheses

Baseline & Data quality analysis

– Calculate and validate baseline network KPIs from available telemetry
– Identify representative network states and historical failure/degradation patterns
– Analyze distributions of latency, reliability, network load and link quality
– Identify data patterns associated with degraded or constrained network states
– Work with ML Engineers to determine whether available data is sufficient for predictive modeling
– Help establish deterministic/statistical baselines before ML approaches are considered

Simulator & Experimental data

– Work with Network/Embedded Engineers to understand simulator outputs and data formats
– Prepare simulator-generated data for analysis and comparison with real-world telemetry
– Support alignment of simulated and field measurements
– Help identify differences between simulated and real-world data distributions
– Support controlled experiment data collection and processing
– Automate repeatable data preparation and analysis workflows

– 4+ years of professional experience in Data Engineering, Data Analytics Engineering or a closely related field
– Strong experience working with time-series and event-based data
– Hands-on experience building data pipelines for telemetry, IoT, infrastructure, industrial systems or other distributed systems
– Strong SQL skills and experience working with large or heterogeneous datasets
– Strong Python skills for data processing, transformation and analysis
– Experience with data normalization, joining, aggregation and validation across multiple sources
– Strong understanding of timestamps, event sequencing and temporal data alignment
– Experience identifying and handling missing, inconsistent and noisy data
– Experience designing analytical data models suitable for downstream analytics and ML
– Ability to understand technical system behavior and translate it into meaningful data structures
– Comfortable working with incomplete documentation and evolving systems

Nice to have:

– Experience with network telemetry or network monitoring data
– Experience with IoT / wireless / sensor networks.
– Experience working with: RSSI, SNR, packet loss, latency, network topology, routing, device availability, battery telemetry
– Experience with distributed systems and event-driven architectures
– Experience with graph/network data models
– Experience preparing datasets for ML/AI experiments
– Experience with network simulators, digital twins or synthetic data
– Experience with cloud telemetry platforms
– Experience in telecommunications, IoT, robotics, industrial systems or defense technology
– Experience with AWS or GCP is a plus

– Python
– SQL
– PostgreSQL / TimescaleDB or similar time-series databases
– Data warehouses / data lakes
– REST APIs
– JSON / structured telemetry formats
– Event-streaming systems
– Pandas / PyArrow or equivalent data-processing tools
– Cloud data services
– Git