Data Engineer

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

- Design, build, and maintain batch and streaming data pipelines that are idempotent, observable, and recoverable. - Model data for analytics (dimensional models, semantic layers, and curated marts), balancing query performance against maintainability. - Integrate data from operational databases, SaaS APIs, files, and event streams, including handling schema drift and late-arriving data. - Build data quality checks (freshness, volume, uniqueness, referential integrity) into pipelines rather than bolting them on afterward, and define how failures alert and escalate. - Own pipelines in production: monitoring, on-call rotation for data incidents, root-cause analysis, and backfills. - Tune performance and cost (partitioning, clustering, file sizing, warehouse and cluster sizing) and make the tradeoffs explicit. - Apply engineering discipline to data: version control, code review, CI/CD, automated testing, and infrastructure as code. - Implement access controls, PII handling, retention, and lineage and audit requirements in partnership with security and compliance. - Partner with analysts, data scientists, and product engineers to turn ambiguous requirements into durable data contracts. - 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

- 5+ years building production data pipelines. - Strong hands-on Python development for data engineering, with real testing, packaging, and code review practice, not scripting alone. - 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. - Strong SQL: window functions, query plans, and performance tuning, not just SELECTs. - Hands-on experience with the Azure data platform: Data Factory, Databricks, Synapse/Fabric, and ADLS. - Solid data modeling fundamentals: normalization, star schemas, slowly changing dimensions. - Git-based workflow and experience shipping through CI/CD. - 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. - Exceptional analytical and problem-solving skills, with the judgment to find the root cause of a data issue rather than patching the symptom. - Strong knowledge and experience in working with customers in a consultative approach in a technical environment.

Additional Qualifications

- Streaming experience (Kafka, Event Hubs). - Lakehouse formats: Delta Lake, Iceberg. - Infrastructure as code (Terraform, Bicep) and containerization (Docker, Kubernetes). - Experience in a regulated environment (HIPAA, SOC 2, PCI, GDPR): auditability, encryption, data residency. - Experience building data platforms for ML or supporting feature pipelines. - Proven ability to manage multiple client projects and deliver high-quality results on time. - Experience in Azure DevOps or GitHub for source control and pipelines.