EverestDX - Lead Data Engineer - ETL/Data Warehousing

3 weeks ago

Hyderabad, India EverestDX Full time

Description : We are seeking a highly skilled Lead Data Engineer to design, build, and scale robust data platforms and pipelines that empower business decisions and analytics.This role combines deep technical expertise with strategic leadership, guiding a team of data engineers to deliver scalable, reliable, and high-performing data solutions.The ideal candidate will have strong experience in cloud data ecosystems, data modeling, data quality frameworks, and cross-functional collaboration.Key Responsibilities : Data Architecture & Engineering : - Design and implement scalable, high-performance data pipelines for ingestion, transformation, and processing of structured and unstructured data.- Architect data lake, data warehouse, and real-time streaming solutions using modern cloud platforms (AWS, Azure, or GCP).- Develop and maintain ETL/ELT processes ensuring data accuracy, consistency, and integrity across systems.- Build and optimize data models (OLAP and OLTP) to support business intelligence, analytics, and reporting.- Evaluate and integrate new data management tools and frameworks to enhance data engineering capabilities.Leadership & Collaboration : - Lead a team of data engineers mentor, review code, and enforce engineering best practices.- Collaborate with data scientists, analysts, and business stakeholders to understand data requirements and translate them into technical solutions.- Establish and enforce data governance, quality, and security standards in alignment with enterprise policies.- Partner with DevOps teams to ensure CI/CD automation, testing, and deployment of data pipelines.Performance & Optimization : - Monitor data pipeline performance and proactively resolve bottlenecks or failures.- Optimize data workflows for cost efficiency, scalability, and processing speed.- Drive adoption of observability and monitoring frameworks (e.g., Datadog, Prometheus, or custom dashboards).Required Skills & Experience : - 8+ years of professional experience in data engineering, with at least 23 years in a lead or senior capacity.- Strong proficiency in Python, SQL, and one or more data pipeline orchestration tools (Airflow, Dagster, Prefect, etc.).- Deep understanding of data warehousing concepts (e.g., Snowflake, Redshift, BigQuery, Synapse).- Experience with streaming technologies such as Kafka, Spark Streaming, or Flink.- Hands-on experience with cloud platforms (AWS, Azure, or GCP) particularly data services like S3, Glue, Dataflow, or Databricks.- Expertise in schema design, data modeling, and performance tuning.- Familiarity with containerization and orchestration tools (Docker, Kubernetes).- Strong understanding of CI/CD principles, version control (Git), and infrastructure-as-code (Terraform, CloudFormation).- Knowledge of data security, encryption, and compliance frameworks (GDPR, SOC 2).Preferred Qualifications : - Experience leading large-scale data platform modernization or migration projects.- Exposure to machine learning pipeline orchestration and MLOps.- Certification in cloud data engineering (AWS Certified Data Analytics, GCP Data Engineer, etc.).- Familiarity with modern data stack tools (dbt, Fivetran, Looker, Power BI).- Strong communication skills and ability to collaborate in cross-functional agile environments (ref:hirist.tech)

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