Find Spark, Kafka, and data lakehouse specialists who build pipelines at petabyte scale.
Updated
Big data engineering is where the volume, velocity, and variety of data exceed what traditional tools can handle. Processing billions of events per day, managing petabyte-scale data lakes, and building real-time streaming pipelines require engineers who understand distributed computing at a fundamental level—not just the API surface of Spark or Kafka.
StepTo places big data engineers from Eastern Europe with companies building data platforms at scale. Poland, Romania, and Serbia have established data engineering communities with experience in enterprise Spark deployments, Kafka streaming architectures, and cloud-native data lakehouses—available at 55–60% below US rates.
Key screen: Spark UI interpretation, not just DataFrame API familiarity
Many developers can write basic PySpark jobs using DataFrame transformations. The critical differentiator is whether they can read a Spark UI DAG, identify a skewed partition causing a stragglerworker, understand why a broadcast join beats a sort-merge join for a small lookup table, and tune executor memory to avoid OOM errors in production. These skills separate notebook users from platform engineers.
Annual base salary in USD/EUR. Streaming specialists and senior platform architects command the upper range.
| Region | Junior | Mid-Level | Senior |
|---|---|---|---|
| United States | $100K–$140K | $140K–$185K | $185K–$235K |
| Canada | $85K–$118K | $118K–$155K | $155K–$200K |
| Western Europe | €72K–€105K | €105K–€145K | €145K–€195K |
| Latin America | $42K–$65K | $65K–$90K | $90K–$120K |
| Eastern Europe | $42K–$65K | $65K–$90K | $90K–$120K |
| Asia | $28K–$48K | $48K–$75K | $75K–$105K |
0–2 years experience
3–5 years experience
6+ years experience
Show a slow Spark job (or Spark UI screenshot with stragglers). Ask them to identify the bottleneck and propose a fix. Can they distinguish data skew from executor memory pressure? Do they know when to broadcast vs sort-merge join?
Design a pipeline: 10TB of logs per day, 24-hour late arrivals allowed, deduplicate events, compute daily aggregates, serve to a dashboard with sub-minute freshness for recent data. Evaluate storage format, processing approach, and orchestration choices.
Present a use case and ask them to justify streaming vs batch. Strong candidates consider: latency requirements, cost, exactly-once semantics complexity, late data handling, and operational overhead. They don't default to streaming for everything.
Describe a pipeline that's been silently producing wrong results for 3 days—a source schema changed and NULLs are now passing through. How do they detect, debug, backfill, and prevent recurrence? Tests end-to-end reliability thinking.
Big data can be expensive. Ask how they'd reduce the cost of a Spark cluster that's running 24/7 but only processing data for 4 hours per day. Do they know spot instances, auto-scaling, right-sizing, and data lifecycle policies for cold storage?
Big data engineers design and build systems that process, store, and serve data at volumes that exceed the capacity of traditional databases—terabytes to petabytes, millions to billions of events per day. Their work includes: batch processing pipelines that transform raw data into analytical-ready datasets (Spark, MapReduce); streaming pipelines that process events in real time (Kafka, Flink, Spark Streaming); data lake architecture (designing zone structures, partitioning strategies, file format selection); data warehouse integration (loading processed data into Snowflake, BigQuery, Redshift); pipeline orchestration (Airflow, Prefect, Dagster); data quality frameworks and monitoring; and performance optimization for large-scale distributed jobs. Big data engineers must understand distributed computing fundamentals—data locality, shuffle operations, partition strategies, and fault tolerance—that don't appear at smaller scales.
Strong Spark engineers understand Spark's execution model deeply, not just its DataFrame API. Key skills: understanding of the Catalyst optimizer and how to write code that produces efficient query plans; partition management (repartition vs coalesce, optimal partition count for shuffle operations); avoiding data skew (salting techniques, AQE configuration in Spark 3.x); join strategies (broadcast join, sort-merge join, when each is appropriate); persistence strategy (cache() vs persist() with appropriate StorageLevels); structured streaming for near-real-time processing with watermarking and late data handling; Delta Lake or Apache Iceberg for ACID transactions on data lakes; and performance tuning via Spark UI—reading DAGs, stages, tasks, and executor metrics. Python (PySpark) is most common; Scala gives deeper framework access. SQL proficiency is essential for Spark SQL usage.
Apache Kafka is a distributed event streaming platform that enables real-time data pipelines and event-driven architectures. At its core: topics (named streams of events), partitions (parallelism and ordering guarantees), consumer groups (parallel consumption with exactly-once semantics), and the Kafka Connect framework for source/sink connectors. You need Kafka expertise when: your system generates continuous event streams (user clicks, IoT sensors, financial transactions, application logs); you're building event-sourced architectures or CQRS systems; you need reliable, scalable message queuing between microservices; or you're implementing CDC (change data capture) from databases. Kafka engineers should understand: topic design (partition count, replication factor), consumer group offset management, Kafka Streams for stateful stream processing, and Schema Registry for schema evolution with Avro/Protobuf/JSON Schema.
Big data engineers are among the higher-paid data professionals due to the combined complexity of distributed systems knowledge and data engineering skills. In the United States, mid-level Spark and Kafka engineers earn $120,000–$175,000. Senior data platform engineers with streaming expertise command $165,000–$235,000. Cloud data platform specialists (AWS EMR, Databricks, GCP Dataproc) fall in similar ranges. Canada runs 15–20% below US. Western Europe: €80,000–€165,000. Eastern European big data engineers—particularly in Poland, Romania, and Serbia where enterprise data platform work is established—earn $50,000–$105,000 per year, a 55–60% saving. Via StepTo, companies access pre-vetted Eastern European big data engineers at $50–$95/hour, with engagements starting in 2–3 weeks.
Both can process streams, but their design philosophies differ. Spark Structured Streaming is a micro-batch engine—it processes data in small batches (configurable from seconds to minutes), offering SQL familiarity, strong batch/streaming unification, and excellent Delta Lake integration. Flink is a true native streaming engine—it processes events one at a time with millisecond latency, offering more sophisticated stateful processing, event-time processing with complex watermarking, and lower end-to-end latency. Choose Spark when: you need near-real-time (seconds) rather than real-time (milliseconds), your team knows Spark already, or you want unified batch/streaming with Delta Lake. Choose Flink when: you need sub-second latency, complex event processing (CEP), or sophisticated stateful computations with exactly-once semantics. Both are valid in 2026; the choice depends on latency requirements and team expertise.
A data lakehouse combines the low-cost storage and flexibility of a data lake with the ACID transactions, schema enforcement, and performance of a data warehouse. This architecture, enabled by table formats like Delta Lake (Databricks), Apache Iceberg (open, multi-engine support), and Apache Hudi (upserts and incremental processing), solves the 'data swamp' problem—data lakes that accumulate unreliable, unorganized data. Key capabilities: time travel (query historical versions of data), ACID transactions (concurrent reads and writes without corruption), schema evolution (add columns without breaking existing queries), and Z-order clustering for query optimization. Databricks (Delta Lake), Snowflake, and cloud-native lakehouses have standardized on Iceberg as a universal format. Big data engineers in 2026 should understand at minimum Delta Lake or Iceberg and how they enable reliable data lake architectures.
The most effective screen combines two exercises. First, a Spark performance optimization scenario: show them a slow PySpark job (a skewed join, excessive shuffles, unoptimized serialization) and ask them to diagnose and fix it. Their ability to read a Spark UI DAG and identify the bottleneck reveals practical experience. Second, a pipeline design scenario: describe a data flow problem (process 100GB of logs daily, with late arrivals up to 24 hours, deduplicate, and serve to a dashboard) and ask them to design the full pipeline—ingestion, storage format, processing engine, scheduling, monitoring, and late data handling. Strong candidates discuss trade-offs, partition strategies, and failure recovery. Beware of candidates who only know the happy path—production big data work is 80% handling failures, late data, and schema changes.
Cloud-native big data has largely replaced on-premise Hadoop clusters. Key platforms: AWS—EMR (managed Spark/Hadoop), Kinesis (managed Kafka alternative), Glue (serverless ETL), S3 (data lake storage), Athena (serverless SQL on S3). GCP—Dataproc (managed Spark), Pub/Sub (messaging), Dataflow (managed Flink/Beam), BigQuery (serverless data warehouse). Azure—HDInsight (managed Hadoop/Spark), Event Hubs (managed Kafka), Synapse Analytics (unified analytics). Databricks spans all three clouds and is the dominant managed Spark and lakehouse platform. Most big data engineers specialize in one primary cloud plus Databricks. Knowledge of Terraform for infrastructure provisioning and dbt or Spark for transformations completes the modern data engineer toolkit.
StepTo matches you with Eastern European big data engineers pre-vetted on real Spark optimization and pipeline design exercises. Engagements start in 2–3 weeks at 55% below US rates.
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