Job Title: Data Engineer Location: Remote Headquarters: Dallas, USA Development Center: Ahmedabad Job Summary: We are seeking a Data Engineer with strong SQL and programming skills (Python/Scala/Java) to design, build, and optimize scalable data pipelines and data platforms. The ideal candidate has experience with ETL/ELT frameworks, distributed processing technologies such as Spark, and cloud-based data services. You will collaborate with cross-functional teams to deliver reliable, high-quality data solutions while ensuring performance, governance, and operational excellence. Experience: 2 years
Core technical skills
- SQL proficiency (non-negotiable; look for window functions, query optimization, not just basic SELECTs)
- At least one strong programming language (Python or Scala/Java)
- Data pipeline/ETL-ELT tools (Airflow, dbt, Dagster, Luigi)
- Distributed processing frameworks (Spark, Flink, sometimes Hadoop for legacy)
- Cloud platforms (AWS, GCP, or Azure) and their data services (Redshift, BigQuery, Snowflake, Databricks)
Data modeling & architecture
- Understanding of dimensional modeling, normalization, star/snowflake schemas
- Experience with both batch and streaming architectures
- Knowledge of data warehousing vs. data lake vs. lakehouse concepts
- Familiarity with file formats (Parquet, Avro, ORC) and why they matter
Engineering fundamentals (separates good from average)
- Version control (Git), CI/CD practices
- Code quality: testing, modularity, documentation
- Infrastructure-as-code (Terraform) and containerization (Docker, Kubernetes)
- Understanding of data quality, observability, and monitoring
Signals of depth vs. surface knowledge
- Can they explain why they chose a tool, not just that they used it?
- Evidence of handling scale (data volumes, latency requirements, cost optimization)
- Ownership of end-to-end pipelines vs. only writing isolated scripts
- Awareness of data governance, lineage, and privacy/compliance
Soft signals
- Collaboration with analysts, data scientists, and business stakeholders
- Ability to translate ambiguous requirements into reliable pipelines
- Communication clarity in how they describe past work
Watch-outs
- Buzzword stuffing without depth (lists every tool but can’t go deep on any)
- Only coursework/certifications with no applied projects
- Confusing data engineering with data analysis or pure ML work
Why Solvative ?
- Top-of-the-line Apple laptops for increased mobility and better productivity.
- Medical insurance for all permanent employees.
- The opportunity of working with an organization that believes in investing in employees’ growth.