Lead Data Engineer: Kafka, Spark & Hadoop (Remote)
Teams Squared
Poland
Remote
PLN 260,000 - 420,000
Full time
14 days+
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Benefits offered by this job
Competitive compensation package
Regular training and professional development initiatives
Job summary
A team augmentation company is seeking a highly experienced Data Engineer Team Lead to spearhead a long-term client engagement. The role requires deep technical knowledge in distributed data processing tools like Apache Kafka, Spark, and Hadoop, along with leadership capabilities to mentor engineers and drive project success. This fully remote position offers a competitive compensation package and opportunities for professional growth. Ideal candidates will have 6+ years in data engineering and strong communication skills.
Qualifications
6+ years of experience in data engineering with recent leadership roles.
Expertise in Apache Kafka, Apache Spark, and Hadoop ecosystems.
Experience with cloud data platforms; BigQuery experience preferred.
Responsibilities
Lead a team of data engineers and contribute to the design of scalable data pipelines.
Architect, implement, and optimize large‑scale streaming and batch data solutions.
Collaborate with teams to define data needs and drive data strategy.
Skills
Leadership skills
Data pipeline optimization
Apache Kafka
Apache Spark
Hadoop
Problem-solving
Communication skills
Education
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
Tools
BigQuery
Cloud data platforms
Job description
A team augmentation company is seeking a highly experienced Data Engineer Team Lead to spearhead a long-term client engagement. The role requires deep technical knowledge in distributed data processing tools like Apache Kafka, Spark, and Hadoop, along with leadership capabilities to mentor engineers and drive project success. This fully remote position offers a competitive compensation package and opportunities for professional growth. Ideal candidates will have 6+ years in data engineering and strong communication skills.