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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in data engineering and software engineering, with a strong focus on designing ETL/ELT pipelines, data models, and integrations. Proficient in AWS technologies and data governance practices, with a proven ability to mentor engineers and lead strategic initiatives in AI and SaaS.
Highest-signal resume keywords
Data Engineering CertificationETL/ELT Pipeline DesignAWS S3, AWS Glue, Amazon AthenaData Quality and GovernanceCI/CD and Infrastructure as Code
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data ModellingSQLPythonJavaApache SparkApache IcebergDataOps PracticesMetadata ManagementQuery OptimizationClassical ML Familiarity
Soft Skills
MentoringLeadershipStrategic Initiative Influence
Tools & Technologies
Qlik CloudGitHubJenkinsAWS DevOpsAirflowDbtSageMakerMLflowKubeflowAmazon OpenSearch
Certifications & Qualifications
Google Professional Data EngineerMicrosoft Certified Fabric Data EngineerAWS Certified Data EngineerDatabricks Certified Data EngineerSnowflake SnowPro Advanced Data Engineer
Industry Keywords
Supply ChainInventoryPricingService FulfillmentCloud Data Migration
Tech Stack
Tools & technologiesAirflowApacheAWSCloudETLJavaJenkinsKubernetesPandasPostgresPythonScikit-LearnSparkSQL
About the role
Key responsibilities & impact- Establish the EazyStock data foundation from raw operational data through production models and ML/LLM-powered features.
- Design and build the EazyStock lakehouse on S3 using Apache Iceberg, AWS Glue, and Amazon Athena.
- Build reliable data integration and ingestion from operational sources.
- Deliver a governed, read-only analytical replica that reduces reporting load on production systems.
- Design partitioning, compaction, snapshot, and retention strategies; own query performance and cloud cost efficiency.
- Deliver clean, well-modelled data into Qlik Cloud and AI/ML consumers.
- Define and operate data quality and governance standards with automated validation and alerting.
- Introduce data contracts, schema evolution discipline, and DataOps practices.
- Establish metadata management, data catalogs, and lineage.
- Build CI/CD for data pipelines, infrastructure, and configuration using GitHub, Jenkins, and AWS DevOps tooling; treat infrastructure as code.
- Implement pipeline observability, including metrics, logging, failure modes, SLAs, and runbooks.
- Design for repeatability and rollback without manual production intervention.
- Build and operate AWS MLOps pipelines for model packaging, deployment, versioning, monitoring, and retraining.
- Support GenAI initiatives through data curation and indexing for RAG, embedding pipelines, Amazon OpenSearch vector storage, and Amazon Bedrock model serving.
- Apply evaluation, observability, cost control, and security practices to AI features.
- Mentor and upskill engineers who are strong developers but new to data.
- Lead strategic initiatives and influence product direction in AI and SaaS.
Requirements
What you’ll need- 6+ years of professional experience spanning data engineering and software engineering, with production systems owned end to end.
- Strong experience designing ETL/ELT pipelines, data models, and data integrations across heterogeneous sources.
- Strong data modelling skills and practical experience with metadata management, data catalogs, and lineage.
- Practical experience with data quality and data governance.
- Experience with data contracts, DataOps practices, and schema evolution in a multi-consumer environment.
- Confidence with DevOps and CI/CD, including Jenkins or equivalent, infrastructure as code, and containerization with Kubernetes.
- Deep practical experience with AWS S3, AWS Glue, Amazon Athena, IAM, Lambda, and cloud workload operations.
- Hands-on experience with Apache Spark and Apache Iceberg or comparable open table formats such as Delta Lake or Hudi, including compaction, partition evolution, and snapshot management.
- Strong SQL and PostgreSQL, including query optimization, indexing, execution plans, replication, and CDC.
- Strong Python and solid production Java.
- Experience integrating with Qlik or another enterprise BI platform.
- Required data engineering certification: Google Professional Data Engineer, Microsoft Certified Fabric Data Engineer, AWS Certified Data Engineer, Databricks Certified Data Engineer, Snowflake SnowPro Advanced Data Engineer, or equivalent.
- Proven ability to mentor and upskill engineers.
- Classical ML familiarity with scikit-learn, XGBoost, and pandas is advantageous.
- Track record of leading a greenfield data platform build or significant cloud data migration is advantageous.
- Experience with Airflow, dbt, or similar is advantageous.
- Experience with SageMaker, MLflow, Kubeflow, or comparable model lifecycle tooling is advantageous.
- Working experience with LLM-based systems, including RAG, embeddings, vector databases, or LLM APIs, is advantageous.
- Practical MLOps experience deploying, versioning, monitoring, and retraining models is advantageous.
- Domain experience in Supply Chain, Inventory, Pricing, or Service Fulfillment is advantageous.
- Passion for the role is welcomed even if all requirements are not met; transferable skills and non-traditional backgrounds are considered.
Benefits
Comp & perks- Impactful Work – Your contributions will directly shape the future of EazyStock’s product and deliver measurable value to customers worldwide.
- Innovation-Driven Culture – Join a team that thrives on creativity, collaboration, and cutting-edge technology.
- Professional Growth – Opportunities to lead strategic initiatives, influence product direction, and expand your expertise in AI and SaaS.
- Global Environment – Work with colleagues and customers across multiple countries in a truly international setting.
- Flexibility – A hybrid work model that supports work-life balance.
- Diversity and inclusion: Syncron values diversity and welcomes candidates, including those with non-traditional backgrounds.
- Transferable skills and shared passion for success are valued.