DataOps Engineer

Job summary

DataOps Engineer We are looking for a mid‑level engineer to build and operate a data platform that uses Apache Iceberg as the lake‑house table format and Docker‑based micro‑services (Spark, Flink, Presto, etc.). you will own the end‑to‑end delivery pipeline, monitoring, security, and incident response, ensuring the platform runs reliably at scale.

Benefits

Qualifications

• Knowledge sharing: keep internal documentation up to date and run short tech demos or brown‑bag sessions on Iceberg, Docker best practices, and automation techniques.
• Minimum
• Bachelor’s degree in Computer Science, IT, Data Engineering, or a related field (Master’s a plus).
• 5 years of hands‑on experience building and operating large‑scale data platforms (lake‑house, data‑warehouse, or big‑data ecosystems).
• Proven production experience with Apache Iceberg (table creation, partition management, schema evolution, catalog integration).
• Strong Docker
• Experience with cloud‑native data services on AWS, Azure, or GCP (EMR, Dataproc, Synapse, etc.).
• Familiarity with other lake‑house formats such as Delta Lake or Apache Hudi and ability to evaluate trade‑offs against Iceberg.
• Knowledge of streaming platforms (Kafka, Pulsar, Kinesis) and real‑time processing patterns.
• Relevant certifications (Databricks Lakehouse Associate, Google Professional Data Engineer, AWS Certified Data Analytics – Specialty, etc.).
• Background supporting data platforms in regulated industries (pharma, finance, healthcare) and understanding of associated compliance frameworks.
• WHY WORK AT BTI?

Responsibilities

• you will own the end‑to‑end delivery pipeline, monitoring, security, and incident response, ensuring the platform runs reliably at scale.
• Iceberg operations: support tables, manage schema changes, partitions, snapshot retention, and keep the catalog (Hive Metastore, AWS Glue, Nessie, …) synchronized.
• Docker image creation & testing: write multi‑stage Dockerfiles for Spark/Flink/Presto, run local test environments with Docker‑Compose, and conduct vulnerability scans (Trivy, Snyk, …).
• Data pipeline development: build ETL/ELT jobs that ingest raw data and write to Iceberg tables; add simple streaming components using Kafka, Pulsar, or Kinesis when needed.
• CI/CD automation: configure pipelines (GitHub Actions, GitLab CI, Azure DevOps, …) to lint Dockerfiles, scan images, version Iceberg metadata, and deploy pipelines without downtime.
• Automation with Ansible/Python: script cluster provisioning, catalog configuration, vacuum/compaction, and other routine housekeeping tasks.
• Observability: instrument services with OpenTelemetry, Prometheus, Grafana, and Loki; create dashboards showing pipeline latency, resource usage, table health, and error rates; set up basic alerts.
• SLA monitoring: measure data freshness, job success rates, and query response times against agreed‑upon targets and report deviations.
• Incident response: join the on‑call rotation, perform first‑line diagnosis and resolution of pipeline failures, Iceberg metadata issues, or container crashes; write concise root‑cause analyses and suggest improvements.
• Security & compliance support: help enforce image signing, mTLS, IAM roles, and bucket policies; collaborate with the security team to meet GDPR, HIPAA, or ISO 27001

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