Build and maintain batch, streaming, and event-driven ingestion pipelines that reliably connect enterprise source systems to the lakehouse.
Develop transformation logic and orchestrated workflows that turn raw source data into usable, governed datasets.
Design and optimize data assets that support analytics, operational reporting, downstream applications, and AI-ready consumption.
Instrument pipelines and data products with validation checks, monitoring, and alerting so data and pipeline issues are detected and resolved quickly.
Tune pipeline, storage, and compute workloads for performance, scalability, and cost efficiency.
Establish engineering standards and reference architectures that allow a lean team to scale responsibly.
Lead technical design reviews and mentor engineers through pairing, code review, and technical guidance.
Qualifications
Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related technical field, or equivalent hands-on experience.
5+ years of production experience in data engineering, data platform engineering, analytics engineering, software engineering, or a related discipline.
Proven experience building and operating large-scale cloud data platforms such as Databricks, Snowflake, Microsoft Fabric, or BigQuery.
Proficiency with SQL, Python, Spark or similar distributed processing frameworks, and modern engineering practices including version control, automated testing, CI/CD, and streaming ingestion.
Experience building reliable, observable pipelines with monitoring, lineage, and data quality controls that operate within enterprise governance, privacy, and security requirements.
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