Data Engineer

Company : Soothsayer Analytics

Working Hours : Full Time

Locations : Hyderabad

Experience : 4–6 Years

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

1. 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.

2. 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.

3. 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.

4. 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

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.