Lead Data Engineer

Trinetix

Lead Data Engineer designing scalable pipelines, models, and analytics-ready datasets for Trinetix, a global enterprise technology services provider. Guiding data architecture, quality, integrations, and engineering teams across client programs.

Posted 8/14/2026full-timeRemote • 🇵🇱 PolandSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data engineering and analytics, with a strong focus on SQL, Python, and modern data platforms like GCP BigQuery and Snowflake. Proven ability to translate business requirements into structured data solutions while ensuring data quality and governance.

Highest-signal resume keywords

Data EngineeringSQL ExpertisePython ProficiencyData Modeling ConceptsExperience with ELT/ETL

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DesignData TransformationData Quality AssuranceData Structuring for AnalyticsComplex Data Transformations

Soft Skills

Collaboration with StakeholdersMentoring EngineersTechnical Guidance

Tools & Technologies

GCP BigQueryDatabricksSnowflakeAirflowDbtSparkKafkaKinesis

Industry Keywords

Data GovernanceMetadata ManagementCross-Domain DataFinancial ServicesData Warehouses

Tech Stack

Tools & technologies

AirflowBigQueryCloudETLGoogle Cloud PlatformKafkaPythonSparkSQL

About the role

Key responsibilities & impact

Lead design and implementation of data pipelines, transformations, and curated datasets
Translate business requirements into structured data solutions and models
Guide and execute development of reusable, analytics-ready data assets
Ensure consistency in how key data elements are defined and used across systems
Collaborate with stakeholders to define and refine data requirements and logic
Review and guide engineering work to ensure quality, performance, and scalability
Establish best practices for data engineering, transformation, and data quality
Support integration of data across multiple domains and systems
Contribute to architecture decisions across data platforms and tooling
Mentor and support other engineers on the team

Requirements

What you’ll need

5+ years of experience in data engineering, analytics engineering, or related fields
Strong SQL expertise and experience designing complex data transformations
Hands-on experience with modern data platforms such as GCP BigQuery, Databricks, or Snowflake
Experience with ELT or ETL, orchestration tools such as Airflow or dbt, Spark, data warehouses and lakehouses, streaming such as Kafka or Kinesis, metadata, and data quality
Proficiency in Python
Strong understanding of data modeling concepts and data structuring for analytics
Experience working with stakeholders to define requirements and deliver data solutions
Ability to balance hands-on delivery with technical guidance
Familiarity with data governance, lineage, or metadata management concepts
Experience working with cross-domain data
Exposure to financial services or other data-intensive industries
Experience supporting migration or evolution of data platforms
Experience with orchestration tools such as Airflow or Cloud Composer
Exposure to data modeling concepts such as dimensional models, data marts, and reusable datasets

Benefits

Comp & perks

Impact at scale: Help shape enterprise AI, software, and data programs across industries
Growth and mastery: Work with a seasoned team from leading consulting and technology backgrounds
Build real products: Work on production ready assets with autonomy over key technical decisions