As a Data Engineering Manager, you'll design, develop, and optimize data pipelines using Apache Spark, Flink, Kafka, and Airflow. Experience with cloud platforms like AWS, GCP, or Azure is essential. You'll lead a team of data engineers and oversee data governance, ensuring compliance and accessibility.

Career Stone Consultant

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 Manager Data Engineering ABOUT THE JOB Role: Manager Data Engineering Reports to: AD - Data Engineering Location: Gurgaon Responsibilities ● As a Data Engineering Manager, you will play a pivotal role in designing the architecture, guiding the development and optimisation of data pipelines and tools. You will mentor the team in building code to efficiently extract raw data, ensuring its quality through robust validation techniques, and leveraging your extensive data expertise to guarantee accurate data ingestion across the pipeline. Your leadership will be instrumental in transforming raw data into formats that are compatible with downstream data sources. ● In this role, you will provide strategic oversight for the development of data tools that are critical for transforming, managing, and accessing data. You will also advise the team on best practices for writing and validating code to ensure the resilience and reliability of data storage platforms.● You will lead the implementation of performance monitoring protocols across data pipelines, guiding the team in building effective visualisations and aggregations that monitor pipeline health. Additionally, you will coach the team on developing and implementing solutions and self-healing processes that minimize points of failure across various product features. ● Your expertise in data governance will be crucial as you help identify needs and oversee the design of data modelling and handling procedures that ensure compliance with relevant laws and policies. You will also ensure that data accessibility is maintained within your assigned pipelines. ● Collaboration with the Data Science team will be key as you work together on feature set requirements and prediction pipelines. You will prepare team members for meetings with key stakeholders, addressing concerns around data requirements and providing expert guidance on feature estimation. Additionally, you will assist in evaluating data costs, access, usage, dependencies across products, and availability for business or customer scenarios related to one or more product features. Required Knowledge and Skills: ● Data Engineering Tools: Proficiency in ETL/ELT processes, with experience in tools such as Apache Spark, Flink, Kafka, and Airflow. ● Programming Languages: Expertise in programming languages like Python, SQL, and Scala for data processing and analysis. ● Cloud Platforms: Strong experience with cloud-based data services such as AWS, Google Cloud Platform, or Azure. ● Data Warehousing: In-depth knowledge of data warehousing solutions like Snowflake, Redshift-Athena, or BigQuery. ● Visualisation Tools: Experience with data visualisation tools such as Tableau, Power BI, or Looker. ● Pipeline Monitoring: Knowledge of monitoring and alerting tools like Prometheus, Grafana, or Datadog. ● Lake house file formats: Knowledge and experience of working with various storage formats like AVRO, HUDI, parquet, orc and delta. ● API design and development: experience in designing, implementing, and optimising REST APIs, including hands-on expertise in developing robust, scalable, and secure API solutions like flask, Django or fastapi.
 
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