TL;DR (what to bet on)

• SQL = non-negotiable. All warehouses, including Snowflake/BigQuery/Redshift.
• Python = glue/orchestration/ML, plus Snowpark Python, dbt, Dagster/Airflow.
• Scala or Java = heavy distributed engines (Spark, Flink, Kafka Streams). Pick one; Scala if Spark-first, Java if broader services.
• Go = fast, tiny services & connectors, infra tools, high-throughput ingestion.
• R = analyst/DS-centric; rarely core to data platforms.
• Julia = niche in DE; great numerics, limited ecosystem/ops maturity.

SQL

What it’s for: Querying, modeling, and transforming data in warehouses/lakes; defining views, materializations, governance.
Shines at: Set operations, window functions, performance via pruning/clustering, readable transformations; runs “where the data lives.”
Typical stack: Snowflake SQL (tasks, streams), BigQuery SQL (scheduled queries), dbt models/tests, Delta/iceberg SQL.
Use it when: Building marts, contracts, or incremental models; enforcing data governance; pushing logic to the warehouse.
Watch-outs: Procedural logic is awkward; versioning/CI need dbt or similar; vendor SQL dialects differ.

Python

What it’s for: Orchestration, ELT/ETL glue, API ingestion, data quality checks, ML/feature pipelines.
Shines at: Rich ecosystem, fast iteration, Snowpark Python, Pandas/Polars for mid-size data, great SDKs/clients, Dagster/Airflow operators.
Typical stack: Dagster/Airflow/Prefect, dbt-core invocations, Snowpark, Pandas/Polars, Pydantic/Pandera, Great Expectations, PySpark for Spark.
Use it when: You need to integrate services, call APIs, validate contracts, run ML, or orchestrate assets.
Watch-outs: Single-threaded by default; heavy CPU work needs vectorization or distributed compute; packaging/venv discipline matters.

Scala

What it’s for: First-class language for Apache Spark and widely used for Flink and Kafka internals.
Shines at: Strong typing + functional patterns on distributed data; best API coverage and performance in Spark.
Typical stack: Spark (DataFrame/Dataset APIs), Flink (DataStream), Kafka Streams, Akka.
Use it when: Your platform is Spark-centric or you maintain streaming jobs at scale (milliseconds/GB-sec matter).
Watch-outs: Steeper learning curve; slower iteration vs Python; hiring pool smaller than Java/Python.

Java

What it’s for: Enterprise services, streaming engines (Flink/Beam/Kafka Streams), connectors.
Shines at: Performance, tooling, long-lived services; widest library support in JVM world; Beam runners.
Typical stack: Apache Flink (primary), Apache Beam, Kafka Streams, Spring Boot ETL services, Iceberg/Delta connectors.
Use it when: Building high-throughput, low-latency streaming jobs or platform services that must be rock-solid.
Watch-outs: Verbose; slower prototyping; data-frame ergonomics worse than Scala for Spark.

Go

What it’s for: Small, fast data services and ingestion daemons; CLI tools; infra around your pipelines.
Shines at: Concurrency (goroutines), tiny static binaries, low memory, quick cold starts—great for high-QPS ingestion and custom connectors.
Typical stack: Custom Kafka/Kinesis producers, HTTP/GRPC data APIs, S3/GCS movers, Terraform helpers, lightweight schedulers.
Use it when: You need a lean service to pull/push data all day with minimal ops overhead.
Watch-outs: Fewer DE-specific libraries; not ideal for complex analytics/ML; generics mature but young.

R

What it’s for: Statistical analysis, exploratory data science, reporting (RMarkdown/Shiny).
Shines at: Stats, visualization (ggplot2), quick analyst workflows; strong packages for time series/biostats.
Typical stack: RStudio/Posit, Shiny apps, DBI/odbc to warehouses.
Use it when: Your stakeholders are analysts who live in R and need to consume warehouse data.
Watch-outs: Not a good fit for building/operating pipelines; weaker orchestration and service tooling.

Julia

What it’s for: High-performance numerics with Python-like syntax; research/prototyping where C-level speed matters.
Shines at: Native speed, multiple dispatch, scientific computing; can be compelling for heavy feature engineering loops.
Typical stack: DataFrames.jl, CSV.jl, Arrow.jl, MLJ.jl; wrappers to Spark/Arrow exist but are niche.
Use it when: You already have Julia expertise and need numeric speed without writing C/Numba.
Watch-outs: Small DE ecosystem, fewer managed services/libraries, less battle-tested ops story.

Practical guidance by platform

• Snowflake: SQL first. Add Python (Snowpark) for UDFs/Stored Procs & orchestration; Scala/Java only if you share code with Spark/Beam or need JVM UDFs.
• Spark (Databricks/EMR): Choose Scala if you live in Spark; PySpark is fine for most, but some APIs/perf land earlier/better in Scala.
• Streaming (Kafka/Flink/Beam): Prefer Java/Scala. Use Python only when latency/throughput demands are modest (PyFlink/Beam can work but watch overhead).
• Microservices & ingestion: Go shines for reliable, low-resource connectors and API shims; Python for fast development if QPS is moderate.

Hiring/maintainability reality

• SQL & Python: biggest talent pool, fastest onboarding.
• Java: abundant enterprise engineers; safe bet for long-lived services.
• Scala: smaller pool but high leverage in Spark shops.
• Go: growing; easy to maintain; great SRE/Platform overlap.
• R/Julia: specialized—don’t build your core pipelines on them.

What to learn (order that pays off)

1. SQL (warehouse + dbt patterns)
2. Python (Dagster/Airflow, Snowpark, Pandas/Polars, contracts/validation)
3. Scala or Java (pick based on Spark vs streaming services focus)
4. Go (optional but valuable for ingestion/services)
5. R/Julia (only if your role overlaps with analytics/research)