Data Engineer

Job Title: Data Engineer

Location: Remote (India)

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.
• An informal work environment that enables you to have fun while being productive.
• Lots and lots of fun activities: we take over one of the nearby restaurants every last Friday of the month, tickets to all Marvel movies for the entire team, company picnics, and more!