Lead Data Engineer
Requirements at a glance
Lead Data Engineer at Arcana Analytics is a role based in Bangalore, Hybrid. The posting asks for 3+ years of experience and a bachelor's degree. It does not mention visa sponsorship. Key skills include SQL, AWS, Git, and MongoDB. Applications are submitted through Greenhouse.
- Experience
- 3+ years
- Education
- Bachelor's degree
- Visa sponsorship
- Not mentioned in the posting
- Application platform
- Greenhouse
Skills in this posting
- SQL
- AWS
- Git
- MongoDB
- Spark
- Electronic health records
- Python
Highlighted skills appear in the posting's requirements.
Extracted automatically from the job description. Confirm details in the full posting below.
Join our dynamic team at the forefront of cutting-edge technology as we seek a seasoned Senior Data Engineer. Embark on a journey where your deep-rooted expertise in computer science fundamentals, alongside an intricate understanding of data structures, algorithms, and system design, becomes the cornerstone of innovative solutions. This pivotal role not only demands your proficiency in developing and elevating compute and I/O-intensive applications but also ensures their peak performance and unwavering reliability.
Responsibilities:
- Develop and implement real-time data ingestion and processing systems.
- Design, build, and operationalize large-scale enterprise data solutions and applications.
- Create and manage production data pipelines, from ingestion to consumption, within a big data architecture using PySpark.
- Leverage expertise in Python, Airflow, SQL, and cloud platforms to build robust solutions (strong knowledge of these is essential).
- Translate complex business challenges into scalable and efficient technical solutions.
- Work collaboratively with a high-performing data engineering team, owning the full lifecycle of solution implementation.
Requirements:
- Bachelor's Degree in Computer Science, Information Technology, or a similar discipline.
- 3 - 8 years of professional experience in data engineering or related fields.
- Proven experience with ETL processes, data integration, and handling large-scale datasets using PySpark.
- Strong understanding of data engineering concepts like ETL, near/real-time streaming, data structures, and workflow management.
- Experience with version control tools like Git/GitHub.
- Proficiency in SQL and Data Warehouse concepts.
- Experience working with SQL or NoSQL databases like Cassandra, MongoDB, or HBase.
- Knowledge of AWS technologies such as EMR, RedShift, Kinesis, Lambda, Glue, S3 IAM, CloudWatch, and big data tools like Hadoop/EMR, Hive, and Sqoop.