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Senior Data Engineer - Data Flow

Data Engineer Remote

Job details

Not specified Salary
Remote Eligibility
Senior Experience
Not specified Employment

About this role

Role overview

This position owns the systems that convert large volumes of messy, changing source data into reliable datasets for analytics and customer-facing products. It is an end-to-end data-engineering role spanning ingestion, transformation, enrichment, validation, publication, orchestration, infrastructure integration, and long-term system design. The work suits someone who is comfortable balancing correctness and timeliness in an environment where inputs and requirements are not always predictable.

Responsibilities

- Operate production data flows across ingestion, transformation, enrichment, validation, publication, and monitoring stages. - Build reusable pipeline patterns for high-volume datasets such as profiles, organizations, job postings, and related enrichments. - Diagnose failures across source data, application code, orchestration, storage, Kubernetes, data warehouses, models, and product expectations. - Investigate data-quality problems, including anomalous metrics and discrepancies affecting users or clients. - Introduce guardrails such as validation checks, lifecycle checks, monitoring, alerts, data contracts, runbooks, and safer release processes. - Improve reliability, performance, and cost efficiency through better resource usage, execution strategies, storage layouts, and orchestration. - Coordinate with acquisition, infrastructure, data-science, economics, and client-facing partners to clarify dependencies and communicate impact. - Use sound judgment about when to patch, roll back, rerun, escalate, or redesign a system.

Requirements

- At least four years of professional experience in data engineering or a closely related field. - Experience owning production data systems beyond narrowly scoped ETL tasks. - Ability to reason about data correctness, operational reliability, and downstream impact. - Strong debugging skills across multiple layers of a data platform. - Clear communication with both technical and non-technical stakeholders. - Comfort working independently in ambiguous, fast-changing conditions.

Skills detected in the listing

PythonGoRustSQLSnowflakeData EngineeringStakeholder ManagementGDPRAWSKubernetesTerraformMachine Learning
Detected Sep 20, 2026
Last verified Sep 20, 2026

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