Solutions Engineer, Media – SQL, Python

SYNCREON

Solutions Engineer curating media datasets for recruitment and staffing services. Requires SQL and Python skills for data engineering in customer-facing environments.

Posted 7/31/2026full-timeRemote • New Jersey • 🇺🇸 United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in curating and delivering media datasets, with strong SQL proficiency and hands-on experience in data engineering and ETL/ELT processes. Capable of collaborating cross-functionally and communicating effectively in customer-facing environments while managing unstructured data.

Highest-signal resume keywords

Strong SQL ProficiencyData Engineering ExperienceETL/ELT KnowledgeCross-Functional CollaborationCustomer-Facing Communication

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

SQLData PipelinesETLELTData EngineeringMetadata Quality TrackingQA WorkflowsScriptingLightweight ToolingContent Curation

Soft Skills

CommunicationCollaborationAdaptabilityProblem-SolvingCustomer Engagement

Tools & Technologies

SnowflakeBigQueryDatabricksAirflowDbt

Industry Keywords

Media DatasetsUnstructured DataEarly-Stage StartupCustomer DemandContent Coverage

Tech Stack

Tools & technologies

AirflowBigQueryETLSQL

About the role

Key responsibilities & impact

Curate and deliver media datasets (audio, video, speech) end-to-end — from ingesting raw partner data to QA-ing and shipping final packages to customers
Translate customer AI data requirements into concrete curation strategies, working hands-on with messy, unstructured, real-world data
Build scripts, lightweight tooling, and repeatable QA workflows to improve delivery speed and consistency
Serve as the internal catalog expert — tracking content coverage, metadata quality, and gaps relative to customer demand
Collaborate cross-functionally with Sales, Product, and Engineering to inform platform roadmap and reduce bespoke delivery work over time.

Requirements

What you’ll need

Strong SQL proficiency — must be comfortable querying large, messy, real-world datasets
Hands-on experience with data pipelines, ETL/ELT, or data engineering (e.g., Snowflake, BigQuery, Databricks, Airflow, dbt)
Comfortable in customer-facing or cross-functional environments — able to communicate technical work clearly to external stakeholders
Experience working at an early-stage startup (Series A–C); no pure big-tech-only backgrounds
Comfort operating with unstructured, imperfect, evolving data — this role requires thriving in ambiguity, not waiting for clean inputs.

Benefits

Comp & perks

Flexible work arrangements