Sr. Data Engineer - Spark

Building high-scale batch and

near-real-time data pipelines deployed on infrastructure we run ourselves

(on-prem), not managed cloud services. You will design and operate high-volume

analytical data systems end to end, with Apache Spark as the core processing

engine for both batch and streaming workloads.

Requirements

• 7+ years

of experience in data engineering and software development

• Ability to

write high-quality code in Java/Scala, Python, or equivalent languages

• Deep,

hands-on production experience with Apache Spark - batch and Spark Structured

Streaming (core requirement)

• Demonstrated

Spark performance tuning: partitioning, caching and persistence, broadcast

joins, shuffle reduction, data-skew handling, and Adaptive Query Execution

• Experience

operating Spark on self-managed clusters (YARN, Kubernetes, or standalone) -

executor sizing, resource allocation, and multi-tenant workloads

• Practical

experience with Kafka (or equivalent messaging systems) as a Spark source and

sink for high-volume workloads, including offset and checkpoint management

• Practical

experience with distributed query engines (e.g., Trino/Presto or similar)

• Practical

experience with ETL / data integration tools, commercial or open-source (e.g.,

Datastage, Informatica, Apache NiFi, or similar)

• Practical

experience with SQL-based transformation frameworks (e.g., dbt or others)

• Strong SQL

skills and understanding of data modeling and data warehousing for analytical

workloads

• Hands-on

experience with real-time / low-latency analytical stores (columnar or OLAP

engines, e.g., Apache Pinot/ClickHouse or similar)

• Practical

experience with big-data platforms and distributions (e.g., Cloudera, Hadoop

ecosystem, Databricks, or similar)

• Practical

experience containerizing and operating data workloads (Docker; Kubernetes a

plus)

• Experience

with workflow orchestration tools (e.g., Airflow or similar)

• Familiarity

with data lake table formats (e.g., Apache Iceberg, Delta Lake, or similar),

including schema evolution and compaction

• Familiarity

with data governance / cataloging tools (e.g., DataHub or similar)

• Familiarity

with lakehouse management systems (e.g., Apache Amoro or similar)

• Familiarity

using AI tools for development and debugging (Claude, Cursor, Codex)