Data Engineer II
unloq · Bengaluru, Karnataka, India
- On site
- Full-time
- $130,000 / year
- Bengaluru, Karnataka, India
Job highlights
- Build and own large-scale data infrastructure.
- Design unified data models and pipelines.
- Process high-volume event and transactional data.
- Collaborate across multiple teams.
- Ensure data reliability and quality.
About the role
Data Engineer II
We are looking for a Data Engineer to build and own a large-scale data infrastructure powering analytics, product insights, and business decision-making. You will define core Sources of Truth (SOT), design unified data models, and build reliable batch and streaming pipelines processing high-volume event and transactional data. This role sits at the intersection of Software, SRE and data engineering, requiring strong foundations in distributed systems, SQL, and big data processing. This role requires the engineer to own the E2E Stack, i. e., from set-up to development of high-volume data pipelines.
Responsibilities
- Build and automate large-scale, high-performance data pipelines (batch and streaming).
- Define and own Sources of Truth (SOT) and dataset design used across multiple teams.
- Streamline ingestion and processing of raw event sources into authoritative event logs.
- Lead data engineering projects, ensuring pipelines are reliable, efficient, testable, and maintainable.
- Design and optimise data models for analytics, reporting, and downstream product use cases.
- Build systems to monitor data quality, data loss, SLAs, and reliability of Tier-1 and Tier-2 datasets.
- Devise strategies to detect, reconcile, and compensate for data loss across multiple sources.
- Evangelise high-quality software engineering practices for data infrastructure at scale.
- Collaborate with Data Science, Analytics, Product, and Engineering teams to align on data architecture.
- Contribute to shared data tooling, frameworks, and standards to improve developer productivity.
Requirements
- 3-5+ years of relevant Data Engineering or Software Engineering experience.
- Bachelor's or Master's degree in Computer Science, Engineering, or equivalent practical experience.
- Strong experience working with large-scale datasets (terabyte to petabyte-scale).
- Solid background in distributed systems design and operation.
- Excellent SQL skills (mandatory); experience with complex analytical queries.
- Solid understanding of data modelling (star/snowflake schemas, fact and dimension tables).
- Hands-on experience with Spark and data processing frameworks.
- Proficiency in one or more programming languages: Java, Scala, or Python.
- Well-versed with tools of one of the Cloud vendors, i. e., AWS, GCP, Azure.
- Experience with ETL frameworks, data pipelines, data lakes, and data modelling fundamentals.
- Strong understanding of monitoring, logging, and observability for data systems.
- Ability to work across teams to define overarching data architecture and influence best practices.
- Strong problem-solving skills and attention to data correctness and reliability.
- Excellent written and verbal communication skills.
Must Have
- Strong understanding of monitoring, logging, and observability for data systems.
- Experience with real-time / streaming data (Kafka, Flink, Beam).
- Familiarity with Hadoop / HDFS ecosystems.
- Experience building or integrating with backend services.
- Exposure to cloud platforms (AWS, GCP, or Azure) includes optimisation (pipelines, cost).
- Exposure to Snowflake, SQL at scale, and modern analytics engineering.
- Exposure to designing building secured data systems using RBAC, CBAC for audit and compliance.
Key skills/competency
- Data Engineering
- Big Data
- SQL
- Distributed Systems
- ETL
- Data Modeling
- Cloud Platforms (AWS/GCP/Azure)
- Spark
- Streaming Data
- Software Engineering
Skills & topics
- Data Engineer
- Data Infrastructure
- Big Data
- SQL
- Spark
- Cloud Engineering
- ETL
- Streaming Data
- Python
- Software Engineering
How to get hired
- Tailor your resume: Highlight relevant Data Engineering, Software Engineering, SQL, and big data experience. Quantify achievements with scale (terabytes/petabytes) and impact.
- Showcase technical skills: Emphasize experience with Spark, distributed systems, cloud platforms (AWS, GCP, Azure), and streaming technologies like Kafka.
- Demonstrate problem-solving: Prepare examples of how you've managed data quality, reliability, and optimized data pipelines.
- Understand the role: Research Unloq's data infrastructure needs and articulate how your E2E stack ownership aligns with their goals.
Technical preparation
Master SQL for complex analytical queries.,Practice building Spark data pipelines.,Understand cloud services (AWS, GCP, Azure).,Familiarize with streaming data concepts.
Behavioral questions
Describe a complex data pipeline you built.,How do you ensure data quality and reliability?,How do you collaborate with other teams?,How do you handle data loss scenarios?
Frequently asked questions
- What is the typical career path for a Data Engineer II at Unloq?
- As a Data Engineer II at Unloq, you'll have the opportunity to deepen your expertise in large-scale data infrastructure. Career progression could lead to Senior Data Engineer roles, specializing in areas like data architecture, platform engineering, or leading complex data projects. Your impact on powering analytics and business decisions will be crucial for growth.
- How does Unloq handle data security for its Data Engineers?
- Unloq emphasizes secure data systems. The role requires exposure to designing and building secured data systems using RBAC and CBAC for audit and compliance, indicating a strong focus on data governance and security practices within their data engineering operations.
- What kind of data scale can I expect to work with at Unloq?
- You can expect to work with substantial data volumes at Unloq. The requirements specify experience with 'large-scale datasets (terabyte to petabyte-scale),' indicating that the data infrastructure you'll be building and maintaining handles immense amounts of information.
- What are the core technologies I'll be using as a Data Engineer II at Unloq?
- Core technologies for this Data Engineer II role at Unloq include SQL (mandatory), Spark, and proficiency in Python, Java, or Scala. You'll also engage with cloud platforms (AWS, GCP, Azure), streaming data tools like Kafka, Flink, or Beam, and the Hadoop/HDFS ecosystems.
- How important is collaboration for a Data Engineer II at Unloq?
- Collaboration is key. The role requires you to work across teams, including Data Science, Analytics, Product, and Engineering, to align on data architecture and evangelize best practices. Your ability to define overarching data architecture and influence best practices will be vital.