Senior Engineer – Data Engineering & Analytics
# Senior Engineer – Data Engineering & Analytics **Millennium IT ESP** · Sri Lanka · `Remote` · `Full Time` 💼 **Уровень роли:** `Senior` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[Data-Engineer]` `[Analytics-Engineer]` `[ETL-Developer]` `[Senior-Data-Analytics-Engineer]` `[Senior-Data-Engineering]` `[Senior-Data-And-Insights-Engineer]` --- ### About the Role Job Description - Design, build, and maintain scalable batch and real-time ETL/ELT pipelines on enterprise data platforms, including Databricks, Snowflake, Microsoft Fabric, Cloudera, Informatica IDMC, and Oracle - Develop lakehouse and data warehouse solutions using Medallion (Bronze/Silver/Gold) architecture on Delta Lake, Apache Iceberg, OneLake, Snowflake, and Oracle Autonomous Data Warehouse (ADW) - Build and orchestrate data workflows using Databricks Lakeflow, Fabric Data Factory, Azure Data Factory, Informatica Cloud Data Integration, Snowflake Streams & Tasks, and Apache Airflow - Implement Change Data Capture (CDC) and streaming ingestion using Oracle GoldenGate, Apache Kafka, and Spark Structured Streaming - Apply dimensional data modelling, including Kimball star schemas, to deliver analytics-ready data marts - Develop Power BI semantic models, including Direct Lake, DAX, and row-level security, in partnership with BI and analytics teams - Implement data governance, security, data quality, and lineage using Databricks Unity Catalog, Microsoft Purview, Cloudera SDX, and Informatica Data Quality - Prepare governed, high-quality data for AI and Machine Learning use cases, including feature pipelines and RAG-ready datasets using vector search capabilities on Databricks, Snowflake Cortex, and Oracle AI Vector Search - Apply DataOps practices, including Git-based version control, CI/CD for data pipelines, automated testing, and Infrastructure as Code - Monitor, troubleshoot, and optimize production pipelines for performance and cloud cost, supporting the practice's 99.90% uptime SLA commitment - Work directly with client stakeholders across the delivery lifecycle, including requirements gathering, data model validation, UAT, Go-Live, and post-Go-Live SLA support - Mentor junior engineers and contribute to internal engineering standards, reusable pipeline frameworks, and technical documentation Person Specification - Possess a Bachelor's Degree in Data Science or a higher qualification, such as an MSc in Data Science, Data Engineering, or Artificial Intelligence, from a recognized university - Have 2–5 years of professional experience in building and operating enterprise data pipelines, data warehouses, or lakehouses - Possess hands-on experience with at least two of the following platforms: Databricks, Snowflake, Microsoft Fabric/Azure Data Services, Cloudera, Informatica (IDMC/PowerCenter), or Oracle (ADW/Exadata/ODI) - Demonstrate strong experience with Apache Spark and distributed data processing at scale - Possess a solid understanding of data modelling, data quality, and data governance principles - Have experience developing Power BI semantic models and reports - Demonstrate strong communication skills and the ability to work directly with client stakeholders - Professional certifications such as Databricks Certified Data Engineer (Associate/Professional), SnowPro Core or SnowPro Advanced: Data Engineer, Microsoft Certified: Fabric Data Engineer Associate (DP-700) or Fabric Analytics Engineer Associate (DP-600), Informatica IDMC, or Oracle Autonomous Database certifications will be considered an added advantage - Experience in migrating legacy ETL platforms such as Informatica PowerCenter, SSIS, or ODI, or on-premises data warehouses to modern cloud lakehouse platforms will be considered an added advantage - Experience with real-time streaming and CDC tools, including Kafka and Oracle GoldenGate, will be considered an added advantage - Exposure to GenAI data engineering, including RAG pipelines, vector databases, and LLM-ready data preparation, will be considered an added advantage - Experience with dbt, Terraform, Azure DevOps, or GitHub Actions will be considered an added advantage - Prior experience in banking, telecommunications, or public-sector data projects will be considered an added advantage - Demonstrate strong SQL and Python (PySpark) skills; knowledge of Scala or Java will be considered an added advantage Originally posted on Himalayas
- Data-Engineer
- Analytics-Engineer
- ETL-Developer
- Senior-Data-Analytics-Engineer
- Senior-Data-Engineering
- Senior-Data-And-Insights-Engineer
Наблюдалась 2026-10-09, впервые 2026-10-09, источник — Himalayas (JSON API).