Coretek is looking for a Data Engineer to build and operate the pipelines and data models that the rest of the business runs on. You’ll own ingestion from source systems through to curated, well-documented datasets that analysts, data scientists, and application teams depend on. This is a hands-on engineering role: you’ll write production code, design schemas, and be accountable for the reliability and cost of what you ship.
Responsibilities
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Design, build, and maintain batch and streaming data pipelines that are idempotent, observable, and recoverable.
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Model data for analytics (dimensional models, semantic layers, and curated marts), balancing query performance against maintainability.
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Integrate data from operational databases, SaaS APIs, files, and event streams, including handling schema drift and late-arriving data.
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Build data quality checks (freshness, volume, uniqueness, referential integrity) into pipelines rather than bolting them on afterward, and define how failures alert and escalate.
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Own pipelines in production: monitoring, on-call rotation for data incidents, root-cause analysis, and backfills.
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Tune performance and cost (partitioning, clustering, file sizing, warehouse and cluster sizing) and make the tradeoffs explicit.
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Apply engineering discipline to data: version control, code review, CI/CD, automated testing, and infrastructure as code.
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Implement access controls, PII handling, retention, and lineage and audit requirements in partnership with security and compliance.
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Partner with analysts, data scientists, and product engineers to turn ambiguous requirements into durable data contracts.
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Maintain data dictionaries, lineage, and pipeline runbooks so consumers can find a dataset, understand what each field means and how current it is, and use it correctly without having to ask the team that built it.
Requirements
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5+ years building production data pipelines.
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Strong hands-on Python development for data engineering, with real testing, packaging, and code review practice, not scripting alone.
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Working knowledge of PySpark: DataFrame and SQL APIs, joins and aggregations at scale, partitioning and shuffle behavior, and the ability to read a Spark UI to diagnose a slow or failing job.
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Strong SQL: window functions, query plans, and performance tuning, not just SELECTs.
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Hands-on experience with the Azure data platform: Data Factory, Databricks, Synapse/Fabric, and ADLS.
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Solid data modeling fundamentals: normalization, star schemas, slowly changing dimensions.
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Git-based workflow and experience shipping through CI/CD.
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Excellent communication skills, with the ability to debug a failing pipeline end to end and articulate the impact to diverse audiences, including non-technical stakeholders.
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Exceptional analytical and problem-solving skills, with the judgment to find the root cause of a data issue rather than patching the symptom.
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Strong knowledge and experience in working with customers in a consultative approach in a technical environment.
Additional Qualifications
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Streaming experience (Kafka, Event Hubs).
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Lakehouse formats: Delta Lake, Iceberg.
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Infrastructure as code (Terraform, Bicep) and containerization (Docker, Kubernetes).
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Experience in a regulated environment (HIPAA, SOC 2, PCI, GDPR): auditability, encryption, data residency.
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Experience building data platforms for ML or supporting feature pipelines.
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Proven ability to manage multiple client projects and deliver high-quality results on time.
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Experience in Azure DevOps or GitHub for source control and pipelines.