Technical Lead
HCLTech
Data Engineer who is able to:
design, build, and automate ETL pipelines that ingest data from multiple sources (e.g., decoding feeds) and deliver reproducible, model-ready datasets build and maintain data warehouses and lakes, plus all supporting database structures provision and run the data infrastructure for storage, scheduling, and orchestration (Airflow) on both GCP and on-prem systems embed rigorous validation, monitoring, logging, and governance to meet GDPR and EU AI Act requirements collaborate with AI engineers to supply high-quality datasets on time Key Skills: Data Engineer, AWS, GCP, Google Cloud Platform, Hybrid, Infrastructure, On-prem, ETL, ELT, Data Pipelines, Data Warehousing, Data Lakes, Airflow, Terraform, Python, SQL, Spark, Pandas, Infrastructure-as-Code, IaC, Pulumi, Ansible, CI/CD, GitHub Actions, Jenkins, Containerization, GDPR, Parquet, Iceberg, SNS, Pub/Sub, Data Validation, IAM.
Screening Criteria:
What data engineering tools or platforms have you used most frequently? How comfortable are you with SQL, and what kind of queries do you typically write? How do you make sure data pipelines are reliable or data quality issues are caught? Who do you typically work with — analysts, data scientists, software engineers
Key Responsibilities
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Other Requirements
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