Senior Level Big Data Engineer Jobs
Senior level big data engineer jobs call for engineers who own architecture decisions, lead cross-functional data initiatives, and set technical direction for the teams building at scale. Openings are concentrated in Technology & Software, with 0% remote or hybrid availability, and employers like Esolvit, TikTok, and KTek Resourcing hiring at this level now.
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Role: Apache Spark / Big Data Administrator
Location: Berkley Heights, NJ (Onsite)
Job Description
- 10+ years solid experience with deploying, configuring, monitoring, and troubleshooting Apache Spark clusters and associated big data frameworks (such as Hadoop, Kafka, or cloud-based data platforms) to ensure high availability and performance.
- Focuses on cluster health, performance tuning, and production data pipelines.
- Technical Experience: Deep hands-on experience with Apache Spark architecture (Driver, Workers, Executors, RDDs/DataFrames) and cluster managers (YARN, Kubernetes, or Mesos).
- OS & Scripting: Strong proficiency in Linux/Unix administration and scripting languages (Bash, Python, or Scala).
- Ecosystem Knowledge: Familiarity with big data and streaming tools like Hadoop HDFS, Kafka, Hive, or monitoring suites (Splunk, Prometheus, Grafana).
- Debugging Skills: Strong analytical capabilities to inspect logs, thread dumps, and metrics under heavy production loads.
- Excellent coordination with stakeholders and communication skills.
Must have experience with below responsibilities
- Cluster Management: Install, configure, and maintain Apache Spark clusters and node infrastructure in production and staging environments.
- Performance & Monitoring: Monitor cluster health, resource utilization (CPU, memory), and execution logs using tools like Spark UI, Splunk, or Elastic. Identify bottlenecks and optimize job execution times.
- Troubleshooting: Diagnose and resolve out-of-memory errors, executor failures, shuffle issues, and node crashes in distributed workloads.
- Pipeline & Integration Support: Support data engineers and developers in managing Spark applications, streaming jobs, and integrations with message brokers like Kafka.
- Security & Access Control: Implement security principles, role-based access control, authentication, and data encryption across the cluster.
- Backup & Disaster Recovery: Execute backup, recovery, and failover plans for Spark applications, configurations, and critical data stores.
- Patching & Upgrades: Apply software patches, driver updates, and version upgrades with minimal disruption to ongoing production jobs.
Role: Apache Spark / Big Data Administrator
Location: Berkley Heights, NJ (Onsite)
Job Description
- 10+ years solid experience with deploying, configuring, monitoring, and troubleshooting Apache Spark clusters and associated big data frameworks (such as Hadoop, Kafka, or cloud-based data platforms) to ensure high availability and performance.
- Focuses on cluster health, performance tuning, and production data pipelines.
- Technical Experience: Deep hands-on experience with Apache Spark architecture (Driver, Workers, Executors, RDDs/DataFrames) and cluster managers (YARN, Kubernetes, or Mesos).
- OS & Scripting: Strong proficiency in Linux/Unix administration and scripting languages (Bash, Python, or Scala).
- Ecosystem Knowledge: Familiarity with big data and streaming tools like Hadoop HDFS, Kafka, Hive, or monitoring suites (Splunk, Prometheus, Grafana).
- Debugging Skills: Strong analytical capabilities to inspect logs, thread dumps, and metrics under heavy production loads.
- Excellent coordination with stakeholders and communication skills.
Must have experience with below responsibilities
- Cluster Management: Install, configure, and maintain Apache Spark clusters and node infrastructure in production and staging environments.
- Performance & Monitoring: Monitor cluster health, resource utilization (CPU, memory), and execution logs using tools like Spark UI, Splunk, or Elastic. Identify bottlenecks and optimize job execution times.
- Troubleshooting: Diagnose and resolve out-of-memory errors, executor failures, shuffle issues, and node crashes in distributed workloads.
- Pipeline & Integration Support: Support data engineers and developers in managing Spark applications, streaming jobs, and integrations with message brokers like Kafka.
- Security & Access Control: Implement security principles, role-based access control, authentication, and data encryption across the cluster.
- Backup & Disaster Recovery: Execute backup, recovery, and failover plans for Spark applications, configurations, and critical data stores.
- Patching & Upgrades: Apply software patches, driver updates, and version upgrades with minimal disruption to ongoing production jobs.
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- Technology & Software
Senior Level Big Data Engineer Jobs: Frequently Asked Questions
How do I get a senior level big data engineer job?
Employers at this level look for engineers who have owned end-to-end data platform design, not just contributed to it. Demonstrable experience leading migrations, optimizing distributed systems at production scale, and mentoring junior engineers separates senior candidates from mid-level ones. A portfolio of measurable outcomes, such as pipeline latency improvements or cost reductions from architectural changes, gives your application a concrete edge.
Which companies hire senior level big data engineers?
Companies hiring senior level big data engineers right now include Esolvit, TikTok, and KTek Resourcing, based on current listings on Migrate Mate as of November 2026. Hiring at this level comes from a mix of large enterprises with mature data platforms, high-growth technology firms scaling their infrastructure, and consultancies staffing senior technical roles for client engagements.
Are there remote senior level big data engineer jobs?
Yes, remote availability at this level is strong. About 0% of senior level big data engineer openings are remote or hybrid as of November 2026, reflecting how distributed data engineering teams have become standard across the industry. Senior engineers with deep platform expertise are often granted the most schedule and location flexibility.
What makes a big data engineer role senior level?
A senior level big data engineer role is defined by ownership and scope rather than task execution. Senior engineers are expected to design distributed systems from the ground up, evaluate and select technologies for the team, resolve ambiguous architectural problems without close supervision, and actively develop the capabilities of less experienced engineers around them. The work shifts from building components to shaping the entire data platform strategy.
Which industries hire the most senior level big data engineers?
Senior level big data engineer roles concentrate in Technology & Software, based on current listings on Migrate Mate as of November 2026. These sectors generate the data volumes and pipeline complexity that require engineers with senior-level architectural judgment rather than execution support.