Data Engineer

Company : Soothsayer Analytics Working Hours : Full Time Locations : Hyderabad Experience : 4–6 Years apply now apply now About The Role Soothsayer Analytics is a global AI & Data Science consultancy headquartered in Detroit, with a thriving delivery center in Hyderabad. We design and deploy end-to-end custom Machine Learning & GenAI solutions—spanning predictive analytics, optimization, NLP, and enterprise-scale AI platforms—that help leading enterprises forecast, automate, and gain a competitive edge. As a Data Engineer, you will build the foundation that powers these AI systems—scalable, secure, and high-performance data pipelines. Job Overview We seek a Data Engineer (Mid-level) with 4–6 years of hands-on experience in designing, building, and optimizing data pipelines. You will work closely with AI/ML teams to ensure data availability, quality, and performance for analytics and GenAI use cases. Key Responsibilities Data Pipeline Development Build and maintain scalable ETL/ELT pipelines for structured and unstructured data. Ingest data from diverse sources such as APIs, streaming, and batch systems. Data Modeling & Warehousing Design efficient data models to support analytics and AI workloads. Develop and optimize data warehouses/lakes using Redshift, BigQuery, Snowflake, or Delta Lake. Big Data & Streaming Work with distributed systems like Apache Spark, Kafka, or Flink for real-time and large-scale data processing. Manage feature stores for machine learning pipelines. Collaboration & Best Practices Work closely with Data Scientists and ML Engineers to ensure high-quality training data. Implement data quality checks, observability, and governance frameworks. Required Skills & Qualifications Education: Bachelor's or Master's in Computer Science, Data Engineering, or related field. Experience: 4–6 years in data engineering with expertise in: Programming: Python, Scala, or Java (Python preferred) Big Data & Processing: Apache Spark, Kafka, Hadoop Databases: SQL and NoSQL (Postgres, MongoDB, Cassandra) Data Warehousing: Snowflake, Redshift, BigQuery, or similar Orchestration: Airflow, Luigi, or similar Cloud Platforms: AWS, Azure, or GCP (data services) Version Control & CI/CD: Git, Jenkins, GitHub Actions MLOps / GenAI Pipelines: Feature engineering, embeddings, vector databases Skills Matrix Skill Details Last Used Experience (Months) Self-Rating (0–10) Python SQL / NoSQL Apache Spark Kafka Data Warehousing (Snowflake, Redshift, etc.) Orchestration (Airflow, Luigi, etc.) Cloud (AWS / Azure / GCP) Data Quality / Governance Tools MLOps / LLMOps GenAI Integration Instructions for Candidates Provide a detailed resume highlighting end-to-end data engineering projects. Fill out the above skills matrix with accurate dates, duration, and self-ratings.