Pay: Salary not listed in the saved posting
Work setup: Unconfirmed
Employment: Unconfirmed

Tools in this posting

Python
SQL
AWS
Azure
Databricks
dbt
Delta
Google Cloud (GCP)
Neo4j
Spark
Tableau
Airflow
PySpark

Source

DATAECONOMY is one of the fastest-growing Data & Analytics company with global presence. We are well-differentiated and are known for our Thought leadership, out-of-the-box products, cutting-edge solutions, accelerators, innovative use cases, and cost-effective service offerings. We offer products and solutions in Cloud, Data Engineering, Data Governance, AI/ML, DevOps and Blockchain to large corporates across the globe. Strategic Partners with AWS, Collibra, cloudera, neo4j, DataRobot, Global IDs, tableau, MuleSoft and Talend. Senior/Lead Data Engineer — Databricks Rutherford, NJ/ Jersey City, NJ Full-time Build scalable, production-grade ETL/ELT pipelines using Databricks (PySpark, Spark SQL, Delta Live Tables, Workflows). Ingest structured, semi-structured, and streaming data into Bronze, Silver, and Gold layers. Develop optimized transformations, data quality rules, and reusable framework components. Implement best practices for job orchestration, monitoring, alerting, and automation. Hands-on experience: Spark, Delta Lake, Workflows, Unity Catalog. Strong SQL programming and performance tuning skills. Experience with cloud environments (AWS/Azure/GCP). Experience with modern data lakehouse concepts and distributed systems. Strong understanding of Lakeflow Connect, LSDP/Lakehouse, Medallion Architecture, Data Validations, Genie, and Agent Bricks/RAG use cases. Should be able to explain these concepts using real project examples and architecture decisions. Knowledge of medallion architecture, DLT and unity catalog within Databricks. Requirements Strong Python (PySpark) and SQL programming Databricks — Spark, Delta Lake, Workflows, Unity Catalog ETL/ELT pipeline development — Medallion Architecture (Bronze/Silver/Gold) Delta Live Tables, Auto-Loader, Structured Streaming Data modeling — dimensional (star/snowflake), normalization/denormalization CI/CD, Git, job orchestration Cloud experience — AWS, Azure, or GCP 7–10+ years in data engineering Knowledge of medallion architecture, DLT and unity catalog within Databricks. Nice-to-Have Skills Lakeflow Connect, LSDP/Lakehouse, Genie, Agent Bricks/RAG use cases Data governance, metadata management, Unity Catalog advanced features Airflow, dbt, or similar orchestration tools Data security, compliance, and access models Cost optimization and performance tuning in cloud environments Corporate/enterprise data warehousing background

Job description

View original posting ↗

DATAECONOMY is one of the fastest-growing Data & Analytics company with global presence. We are well-differentiated and are known for our Thought leadership, out-of-the-box products, cutting-edge solutions, accelerators, innovative use cases, and cost-effective service offerings. We offer products and solutions in Cloud, Data Engineering, Data Governance, AI/ML, DevOps and Blockchain to large corporates across the globe. Strategic Partners with AWS, Collibra, cloudera, neo4j, DataRobot, Global IDs, tableau, MuleSoft and Talend. Senior/Lead Data Engineer — Databricks Rutherford, NJ/ Jersey City, NJ Full-time Build scalable, production-grade ETL/ELT pipelines using Databricks (PySpark, Spark SQL, Delta Live Tables, Workflows). Ingest structured, semi-structured, and streaming data into Bronze, Silver, and Gold layers. Develop optimized transformations, data quality rules, and reusable framework components. Implement best practices for job orchestration, monitoring, alerting, and automation. Hands-on experience: Spark, Delta Lake, Workflows, Unity Catalog. Strong SQL programming and performance tuning skills. Experience with cloud environments (AWS/Azure/GCP). Experience with modern data lakehouse concepts and distributed systems. Strong understanding of Lakeflow Connect, LSDP/Lakehouse, Medallion Architecture, Data Validations, Genie, and Agent Bricks/RAG use cases. Should be able to explain these concepts using real project examples and architecture decisions. Knowledge of medallion architecture, DLT and unity catalog within Databricks. Requirements Strong Python (PySpark) and SQL programming Databricks — Spark, Delta Lake, Workflows, Unity Catalog ETL/ELT pipeline development — Medallion Architecture (Bronze/Silver/Gold) Delta Live Tables, Auto-Loader, Structured Streaming Data modeling — dimensional (star/snowflake), normalization/denormalization CI/CD, Git, job orchestration Cloud experience — AWS, Azure, or GCP 7–10+ years in data engineering Knowledge of medallion architecture, DLT and unity catalog within Databricks. Nice-to-Have Skills Lakeflow Connect, LSDP/Lakehouse, Genie, Agent Bricks/RAG use cases Data governance, metadata management, Unity Catalog advanced features Airflow, dbt, or similar orchestration tools Data security, compliance, and access models Cost optimization and performance tuning in cloud environments Corporate/enterprise data warehousing background