Data Observability Engineer

We are looking marketing profile for a Senior Data Engineer / Technical Lead to architect and lead enterprise-scale data engineering solutions for a leading technology and telecommunications organization. The ideal candidate will have strong expertise in Snowflake, Databricks, Python/PySpark, SQL, Azure, streaming, CI/CD, data quality, and enterprise data platforms , along with strong finance and revenue data experience. Candidate Requirements Senior-level experience in Data Engineering / Technical Leadership . Strong experience with Snowflake and Databricks . Advanced Python/PySpark and SQL skills. Strong Azure/cloud data platform experience. Experience with ETL/ELT, batch, incremental, CDC, and streaming pipelines . Experience with Kafka / Event Hub, Spark Structured Streaming, Airflow, and ADF . Strong understanding of CI/CD, DevOps, data quality, security, and governance . Experience with finance/revenue data , billing, GL, reconciliation, and financial reporting. FAANG experience mandatory ; Tier 1 product-based companies may be considered. Required Skills Data Engineering & Technical Leadership Snowflake Databricks / PySpark Python / Advanced SQL Azure / ADLS Gen2 Azure Data Factory / Airflow Kafka / Azure Event Hub Spark Structured Streaming dbt Delta Lake / Unity Catalog ETL / ELT / CDC CI/CD / Azure DevOps / GitHub Actions Terraform / Bicep Data Quality & Observability Data Modeling Data Security & Governance Finance & Revenue Data Required Qualifications Proven experience designing and leading enterprise-scale data pipelines . Strong hands-on experience with Snowflake, Databricks, Python/PySpark, and SQL . Experience with scalable batch, incremental, CDC, and real-time data processing . Strong Azure experience with services such as ADLS Gen2, Event Hub, ADF, and Key Vault . Experience with Airflow/dbt and modern data engineering frameworks. Strong knowledge of data quality, testing, observability, monitoring, and data governance . Experience with CI/CD, infrastructure-as-code, and deployment automation . Understanding of RBAC, PII/CPNI, data lineage, auditability, and secure pipeline design . Experience with enterprise data models, SCD Type 1/2, partitioning, clustering, and performance optimization . Strong knowledge of billing, revenue, GL, reconciliation, revenue recognition, and financial reporting . Strong communication, analytical, problem-solving, and technical leadership skills. Bachelor's degree in Computer Science, Information Systems, Engineering, or related field preferred. Preferred Qualifications Experience with FAANG companies Facebook/Meta, Amazon, Apple, Netflix, or Google . Experience with Tier 1 product-based companies . Advanced experience with Snowflake Snowpipe, Streams, Tasks, and optimization . Strong Databricks, Delta Live Tables, Unity Catalog, and PySpark experience. Experience with dbt, Great Expectations, and data observability frameworks . Experience with real-time finance/revenue data processing . Experience with Terraform/Bicep and cloud infrastructure automation . Experience leading teams, mentoring engineers, and driving engineering best practices. Key Responsibilities Architect and lead enterprise-scale ETL/ELT pipelines for high-volume finance and revenue data. Design scalable batch, incremental, CDC, event-driven, and streaming solutions . Lead architecture and adoption of Snowflake and Databricks . Establish best practices for PySpark, SQL, dbt, Airflow, ADF, and cloud data platforms . Architect real-time solutions using Kafka, Event Hub, and Spark Structured Streaming . Drive data quality, testing, observability, SLA monitoring, and data governance . Define CI/CD, DevOps, code quality, and release management standards. Implement enterprise security controls including RBAC, PII/CPNI compliance, secrets management, lineage, and auditability . Support data modeling, performance optimization, partitioning, and clustering. Provide technical leadership for HLD/LLD, design reviews, code reviews, testing, and releases . Partner with architects, developers, product managers, analysts, and business stakeholders. Lead incident reviews, defect RCA, troubleshooting, and continuous improvement . Mentor team members, provide technical guidance, and drive engineering excellence. Communicate complex technical solutions clearly to technical and non-technical stakeholders.

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